This is particularly true in China, a large and economically diverse country that has rapidly industrialized and urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city-level estimates of CO2 emissions for 182 Chinese cities, decomposed into 17 different fossil fuels, 46 socioeconomic sectors, and 7 industrial processes. We find that more affluent cities have systematically lower emissions per unit of gross domestic product (GDP), supported by imports from less affluent, industrial cities located nearby. In turn, clusters of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption-based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector-based analysis of each city suggests that technological improvements could be a practical and effective means of reducing emissions while maintaining growth and the current economic structure and energy system. We explore city-level emission reductions under three scenarios of technological progress to show that substantial reductions (up to 31%) are possible by updating a disproportionately small fraction of existing infrastructure.As national efforts to reduce CO For example, Parshall et al.  administrative territorial approach, which is compatible and consistent with national and international emission inventories. The 46 sectors are classified according to China’s System of National Accounts . The sectors classification can also be mapped with other countries/cities around the world . According to the latest administrative planning report, there are currently 334 cities in China. We select 182 cities based on the data availability . The 182 case cities encompass 62% of China’s population as well as 77% of its GDP in 2010.We first classify cities into five groups according to their apparent development pathway and then quantify the potential for emission reductions among the city groups under a range of technological scenarios. Our results reveal the extent to which different policies may reduce emissions while in large part maintaining the current industrial structure and energy mix of cities and thereby minimize economic impacts.2 emissions of 182 cities, which are for 2010. Most of the 182 cities are located in the eastern half of the country  of the cities’ total emissions, with oil and natural gas combustion representing just 15 and 2%, respectively, and the remaining 9% from industrial processes such as cement production.2 emissions of the 182 cities range from <15 Mt (yellow) to >90 Mt . Higher levels of emissions prevail in the north and east, and the top-emitting cities represent a disproportionately large fraction of the total emissions from the 182 cities. The top five emitting cities  accounted for 11% of the total in 2010. In addition, our estimates of CO2 emission intensity, calculated as total emissions divided by GDP, range from <0.1 to >1.5 metric tons of CO2 per ¥1000, with a minimum of 0.04 and 0.05 metric tons of CO2 per ¥1000 in Shenzhen  and Huangshan , respectively; a maximum of 1.72 and 1.55 metric tons of CO2 per ¥1000 in Hegang  and Panzhihua , respectively; and an average of 0.22 metric tons of CO2 per ¥1,000 (see table S1).Population and socioeconomic development varies tremendously among these cities: from 0.2 million people living in Jiayuguan  to 28.7 in Chongqing (southwest) and from per-capita GDP of just ¥9068 in Fuyang  to ¥175,125 in Ordos . Similarly, colors in 2 per ¥1000, respectively, have small manufacturing and energy sectors  and larger service sectors . In contrast, cities such as Maanshan, Tangshan, and Panzhihua have iron and steel production as their “pillar” industries , with correspondingly high carbon intensities: 0.68, 0.43, and 1.55 metric tons of CO2 per ¥1000, respectively. Similarly, in cities where energy production and mining of natural resources are the dominant industries, such as Hegang, emission intensities are especially high (1.72 metric tons of CO2 per ¥1000) because the activities are emission-intensive, but such cities produce low value-added energy products .The detailed investigation of cities’ emissions by sectors and fuels helps in understanding the wide range in cities’ carbon intensity: cities such as Beijing and Shenzhen, whose emission intensities are just 0.07 and 0.04 metric tons of COn = 8), high-tech cities (n = 24), energy production cities (n = 32), heavy manufacturing cities (n = 51), and light manufacturing cities (n = 67) . Recognizing these characteristic differences, we use formal cluster analysis to classify the cities into five distinct groups based on their GDP and industrial output: service-based cities . The SDs of the energy production and heavy manufacturing cities’ emission intensity are the largest among the five city groups . The SDs of high-tech and service-based cities’ emission intensities are relatively small, which are 0.08 and 0.07 metric tons of CO2 per ¥1000, respectively.2 with low economic outputs. Conversely, high-tech and service-based cities rely more on the high-tech industries and service sectors. This led them to lower emission intensities. Such a difference in city groups’ energy intensity is determined by the cities’ economic structures. The energy production and heavy manufacturing cities have more energy-insensitive sectors, which emit high COz test of mean values  define the pillar industry of each city group. A city group’s pillar industry has the highest share in economic structure compared with other city groups. For example, the service sectors’ average share in GDP of the service-based cities (59%) is significantly higher than those of energy production (34%), heavy manufacturing (36%), light manufacturing (36%), and high-tech (43%) cities. Similarly, the energy production sectors’ average share in energy production cities (54%) is the highest among the five city groups.2 emissions, we next examine the potential for emission reductions in industrial sectors through scenarios of specific technological improvement. As each city has 39 industrial sectors , there are 7098 industrial city sectors in total. The term “city sector” refers to each industrial sector of each city; for example, thesector “coal mining and dressing” of Beijing is a city sector, while the sector “food processing” of Shanghai is another. We calculate the per–industrial output emissions for the 7098 industrial city sectors. We then identify three levels of superemitters of those city sectors based on the city sectors’ per–industrial output emissions  Above-average emitters have per–industrial output emissions greater than the sector mean.(2) One SD superemitters have per–industrial output emissions 1σ above the sector mean.(3) Two SD superemitters have per–industrial output emissions 2σ above the sector mean. The 2 SD superemitters represent the most carbon-intensive city sectors.2 emissions  Scenario #1: 2 SD superemitting city sectors reach the current national sector average emission intensity.(2) Scenario #2: 1 SD superemitters reach the current sector average.(3) Scenario #3: Above-average superemitters reach the current sector average.2 in energy production cities, 388 Mt of CO2 in heavy manufacturing cities, 36 Mt of CO2 in light manufacturing cities, and 1 Mt of CO2 in high-tech cities, or 52, 38, 40, and 11%, respectively, of those cities’ pillar industry emissions. Energy production and heavy manufacturing sectors are the primary emission sources of every city group due to their high emission intensities; therefore, we calculate the emission reduction capacities of the cities’ energy production (fig. S3B) and heavy manufacturing sectors (fig. S3C) as well.We then calculate the emission reduction capacities of the five city groups’ pillar industry, under the three scenarios (shown in fig. S3A). Under the strongest scenario, #3, reductions to cities’ pillar industries alone could avoid emissions of 544 Mt of CO2 emissions, particularly in heavy manufacturing and light manufacturing cities  unchanged. This is critical, given the rapidity of targeted reductions; the central government in China seeks to reduce national CO2 emissions decreased by 7.6 Mt during 2010 to 2015, but emissions in Hebei province increased by 87.1 Mt during the same period . Although a few affluent cities have reduced the proportion of coal in their energy mix [for example, Beijing has reduced its coal consumption by 61%, or 18.2 Mt from 2007 to 2015 , more affluent, service-based cities might be able to quickly outsource their more emission-intensive industries without economic hardship. However, such outsourcing by highly developed cities could increase overall emissions; by virtue of their level of development, these cities often have more advanced technologies in place, and outsourcing would thus tend to move carbon-intensive, heavy-polluting industries to less-developed regions with less-efficient technologies. For example, Shougang Corporation, one of the largest steelmaking companies in China, has moved progressively from Beijing to Hebei province (mainly to Tangshan) since 2010. Beijing’s COOn the basis of detailed analysis of cities and their industries, our findings suggest that China’s near-term goals of reducing its emission intensity may be feasibly accomplished by targeted technological improvements, buying time for the longer-term strategies of shifting to non-fossil energy and a more service-based economy. Moreover, improving and optimizing the energy and carbon efficiency of industrial production processes and operations could help lower the costs of advanced technologies and thus facilitate their deployment in less-developed cities and countries beyond China.2 emission intensity by 60 to 65% and running a nationwide Emission Trading Scheme over the next few years, policy-makers in the country need increasingly specific, subnational information about the sources of CO2 and the potential reductions and economic implications of different possible policies. By categorizing Chinese cities according to their development stage and industrial makeup, we offer policy-makers an opportunity to meaningfully differentiate across the wide range in city-level CO2 emissions (from 1.6 to 194 Mt) and emission intensity (from 0.04 to 1.72 metric tons of CO2 per ¥1000). Further, because the lower emission intensities of affluent cities  are supported by imports from less affluent, industrial, and energy-producing cities located nearby, consumption-based policies may allow more developed cities to subsidize emission reductions without undercutting the economic core of still-developing cities by directly regulating their manufacturing and the electric sectors.In order for China to cost-effectively reach its goal of reducing COHowever, where policies directly targeting manufacturing and electric power infrastructure are implemented, our sectoral analysis of each city indicates that targeted technological improvements may be a practical and effective means of reducing emissions while preserving cities’ current economic structure and energy systems. In particular, by focusing efforts on superemitting industry sectors in each city, roughly 30% of China’s carbon emissions might be eliminated.2 emissions in recent years is a tremendous watershed in the global effort to avoid dangerous climate change, the progress reflects sweeping policies to improve the country’s industrial technologies and energy systems. Further progress will increasingly depend on policies that differentiate among cities according to their economic structure, level of development, and infrastructure and are carefully crafted to target the largest and most cost-effective emission reductions.Although the leveling off of China’s COIn addition, rapidly growing cities with “new” investments in infrastructure and institutions tend to emulate already-established cities and thereby lock into the “old” development paths. Instead, promoting low-carbon transitions at early stages of industrialization implies careful consideration of “future fit”—how industries can be designed and run to optimize or decouple the relationship between their economic productivity and energy use . The socioeconomic and environmental characteristics of the 182 cities represent various stages of industrialization, from the preindustrialization stage  to the postindustrialization stage (such as Beijing and Shanghai). In turn, this cross section of Chinese cities we analyze may provide important insights for other developing countries seeking to target superemitting sectors/units in their cities, perhaps enabling them to bypass or abbreviate the most emission-intensive phases of industrialization.2 emissions of one country: the territorial-based, production-based, and consumption-based approaches. According to the IPCC  and the World Business Council for Sustainable Development  from the total consumption; this part did not emit CO2 either  and the corresponding emission factors. The method is widely used by researchers. On the basis of the IPCC method, the National Development and Reform Commission of China (NDRC) designed an emission account system for Chinese provinces  were calculated in process) were calculated in Here, we adopted the IPCC and NDRC methods. The COij refers to the CO2 emissions by fossil fuel type i and sector j; ADij, the activity data, means the corresponding fossil fuel consumption. NCVi, CCi, and Oij are known as emission factors. NCVi refers to net caloric value, which is the heat value produced per physical unit of fossil fuel combusted (in J/ton); CCi (carbon content) is the CO2 emissions per net caloric value produced from a given fossil fuel type i (in metric tons of CO2/J); Oij is the oxygenation efficiency, which refers to the oxidation ratio when fossil fuels are burned (in %).In i and CCi were collected on the basis of our previous investigations on China’s fossil fuel quality  were collected from IPCC  and 7 industrial processes . The fossil fuel–related emissions were calculated on the basis of 46-sectoral energy consumption; therefore, the cities’ emission inventories were constructed by 46 socioeconomic sectors , corresponding to the national sectors classification defined by the National Administration for Quality Supervision and Inspection and Quarantine (see table S2) .The 46 sectors include 1 farming, 1 construction, 3 service sectors, 2 household energy use , and 39 industrial sectors. The 39 industrial sectors can be grouped into four categories: energy production, heavy manufacturing, light manufacturing, and high-tech industries. The energy production category includes five sectors that produce either primary or secondary energy types, while the high-tech category refers to five sectors aiming at high and new technical industries. The remaining 29 sectors belong to manufacturing sectors, including both heavy and light manufacturing sectors. Here, we defined the heavy manufacturing category to include 16 sectors that input energy to produce intermediate products, such as “ferrous metals mining and dressing” and “nonmetal mineral products.” We classified 13 sectors as light manufacturing sectors, which mainly produce final products such as food processing and furniture manufacturing. We allocated the COij) were collected on the basis of the “energy balance table” and “industrial sectoral energy consumption table” from the city’s statistical yearbook. We collected the energy consumption data of the nonindustrial sectors  from the energy balance table. The energy balance table illustrates the energy data of one region, such as production, import, input and output transformation, final consumption, and loss (see table S5). Because of the poor data quality at the city level, only 10% (or 17) of the 182 cities have the energy balance table in their statistical yearbooks. The remaining 90% (or 165) of the cities do not have the table. Considering the completeness and consistency of cities’ emission inventories, we followed our previous study to scale down the corresponding provincial tables to obtain the city-level energy consumption data of these nonindustrial sectors . Here, we collected the energy consumption of the cities’ 39 industrial sectors from the industrial sectoral energy consumption table, which were investigated by the cities’ statistics office at the enterprise level.t) were collected from cities’ statistical yearbooks directly. Cities’ population, GDP, industry output, and other socioeconomic data were collected from cities’ statistical yearbooks and China City Statistical Yearbook 2011. Results and original data were freely available for download from China Emission Accounts and Datasets  after registration.The industrial productions of cities  were allocated to the power plants, rather than to their final consumption sectors. The scope and framework were consistent with the NDRC guidelines for provincial greenhouse gas inventories, which were widely used in China’s emission accounts and studies  of 182 cities fall in . The highest uncertainty appeared in Hegang , while the lowest appeared in Huizhou . Energy production and high emission cities usually have a relatively higher uncertainty of COIn our previous research, we compared our estimation of five cities’ emission with the calculation through a bottom-up approach  in the following steps:EP), heavy manufacturing (IOHM), light manufacturing (IOLM), and high-tech industry (IOHT). Taking Beijing as an example, IOEP−Beijing is equal to the energy productions’ industrial outputs divided by Beijing’s total industrial outputs (28%). IOHM−Beijing, IOLM−Beijing, and IOHT−Beijing are 37, 11, and 24, respectively, in 2010. This means 28% of Beijing’s industrial outputs are contributed by energy productions, while 24% are contributed by high-tech industries.(1) We calculated each city’s industrial output share (IO) of the four manufacturing categories: energy production  We then sorted the cities according to their sectors’ industrial output share from small to large and obtained the cities’ rankings of each manufacturing category. Taking Beijing as an example, IO(3) We changed the rankings into percentiles (IO′). Beijing’s high-tech industry share is ahead of 90.11% of the case cities .As the cluster analysis was operated on the basis of mathematical relationships, the results may be distinct with the practical situation. We adjusted the results slightly according to other information and the actual situations in this empirical analysis. For example, Xianyang was moved from group 4 to the energy group due to its high ranking in the energy sector category (88) and huge absolute value of energy production output .Following the cities’ industrialization process, the most developed high-tech cities will further develop into service-based cities. These service cities may go through every stage of the industrialization process in the past decades, and they have now successfully transferred their pillar industry to service sectors with low emission intensity. They presented sophisticated and practical roadmaps of economic structure optimization and low-carbon development. Therefore, this study extracted these service-based cities from the previous high-tech group to form a new group. Here, the service-based cities were defined as high-tech cities with a higher than 50.63% service sectors’ share in GDP. The 50.63% boundary was calculated as the high-tech cities’ average service sectors’ share in GDP . To validate the city group, we applied the z test of energy production sectors . Moreover, the sectors’ average share was also significantly different between the energy production and light manufacturing cities.In  sectors , light m sectors , and hig sectors . As for z test for the mean value between city groups verified the city clusters. The pillar industry of a city group was significantly higher than the others.Above all, the This study has some limitations, which mainly lie in the accuracy of the cities’ emission accounts. Further work should focus on the following three aspects to improve the accuracy of the cities’ emission accounts, as well as their emission reduction potentials.First of all, the downscale emission inventories developed in this study should be compared with the bottom-up emission inventories. As discussed earlier, the bottom-up emissions  and their emission-saving potentials. This was primarily restricted by the emission data for nonindustrial sectors. Further studies should try to improve estimation of emissions in urban service sectors. We would like to conduct a large survey to measure the differences between emission intensities of service-related sectors across 50+ selected cities in China.


Lambs are simultaneously subjected to several routine husbandry practices that cause pain. One of the main factors that limit the use of analgesics in lambs is the difficulty in pain recognition by sheep farmers. This study aimed to determine how husbandry practices are carried out in Chilean farms, the sheep farmers’ perception of animal welfare and pain, and the factors that affect them, as well as the level of agreement among farmers in the recognition of pain associated with these practices. Farmers were invited to participate in a workshop and they were asked through a survey about their sociodemographic information, how husbandry practices are being performed in their farms, and were asked to score the intensity of pain associated to seven of these practices. Castration and tail docking were perceived as the most painful practices and farmers agreed among them that these routine husbandry practices cause severe pain to animals. Several factors were associated with the farmers’ pain perception, such as the method used for the specific husbandry practices and the farmers’ educational level. In general, routine husbandry practices were carried out without using analgesics and with painful methods despite the agreement among farmers regarding the recognition of pain associated with these procedures.p < 0.05). Pain perception was associated with the method used for the specific husbandry practices, the farmers’ educational level, the farm size, and flock size (p < 0.05). There was a fair to good level of agreement beyond chance (p < 0.05) in the recognition of pain associated with the most painful practices. In general, husbandry practices are not carried out in young animals, use painful methods, without using analgesics, which may have a negative impact on animal welfare.Considering the public concern about the welfare of farm animals during routine husbandry practices, this study aimed to determine how husbandry practices are carried out in Chilean farms, sheep farmers’ perceptions of animal welfare and pain, and factors that affect them, as well as the level of agreement among farmers in the recognition of pain associated with these practices. Using a self-administered survey, participants were asked about their sociodemographic information, how husbandry practices are carried out in their farms, and their pain perception for seven of these common husbandry procedures using a numerical rating scale (0 to 10). A total of 165 farmers completed the survey and perceived castration and tail docking as the most painful practices in lambs (median pain score 10 vs. 8,  Pain is defined as a complex and subjective experience associated with actual or potential tissue damage , which nIn production systems, sheep may experience pain due to different diseases, e.g., mastitis and lameness . Also, lScientific evidence has shown that analgesics reduce pain during husbandry procedures ,16,17. TPain evaluation and recognition in lambs is a complex process, mainly due to the fact that active pain behaviors in these animals are not as intense as in other species . MoreoveThis study was approved by the Committee of Bioethics and Use of Animals in Research of the Universidad Austral de Chile (Nº 288/217). The study was conducted between November 2016 and May 2018. A self-administered survey was applied to sheep farmers based in three Chilean regions: Magallanes and the Chilean Antartic , Aysén , and Libertador Bernardo O’Higgins . These regions were selected because they have the largest number of sheep and flocks of the country . ThroughThe survey consisted of three sections. In the first section, open-ended questions about the farmers’ sociodemographic information were obtained, including gender, age, region, educational level, as well as information related to their time experience as a sheep farmer (years), farm size (hectares), and flock size . Also, farmers were asked to select one or more options regarding if their farm had technical advice in the following areas: Nutritional, reproductive, health, or none of them.The second section of the survey contained open-ended questions of how sheep farmers carry out the following husbandry practices in lambs: Animal identification, tail docking, and castration. For each procedure, they were asked by open questions if they carry out each practice, and if they do, “at what age they carry it out”, the method used, the reasons for performing the practice, person responsible for performing the procedure, and if they use analgesics or not. Additionally, for tail docking, a diagram was used to mark the length at which the tail was docked and to justify the answer. The diagram was adapted from Fisher et al. , and farThe third section aimed to establish the farmer’s knowledge concerning animal welfare, including individual perception and importance. Farmers were asked if they had previously heard about animal welfare (yes/no), and to choose what animal welfare means to them by selecting one or more of the following options: Animals do not suffer from hunger and thirst; animals do not suffer from pain, injury, and diseases; animals do not suffer from discomfort; animals do not suffer from fear and distress; animals can express normal behavior. Participants were also asked to select one or more options from a pre-defined list regarding the importance of having good animal welfare in their farms, considering the following alternatives: It affects animal health and production; it influences meat quality; it is a consumer requirement; or animal welfare is indifferent to me. Also, they were asked to select and rank in order of importance the three main animal welfare issues in their farms from a pre-defined list: Predation, lamb mortality, rounding up/transportation, husbandry practices, slaughter, facilities, nutrition/water availability, and diseases.Finally, sheep farmers were asked to describe how they perceive the intensity of pain associated with each of the following husbandry procedures: Animal identification, tail docking, castration, hoof trimming, deworming, vaccinating, and shearing. For this, a table with an eleven-point (0 to 10) numerical rating scale (NRS) was used, considering 0 as “no pain’’ and 10 as the maximum pain .p < 0.05) were used to determine the association of the method used for each husbandry practice with the sheep farmers’ pain perception. Linear regression models with Poisson distribution were fitted to assess the effect of the continuous variables  and factors  on the pain scores associated to each husbandry practice.Data of a total of 165 completed surveys were analyzed using the statistical program, R  . DescripTo estimate the degree of agreement among sheep farmers in the recognition of pain associated to husbandry practices, pain scores of NRS were categorized as follows: No pain = 0; Mild to moderate pain = 1 to 5; and Severe pain = 6 to 10. The degree of agreement beyond chance was estimated by the Fleiss’ kappa coefficient (κ) for multiple raters using Epidat 4.1 software, where κ values: <0.40 indicated poor agreement; between 0.40 and 0.75 fair to good agreement; >0.75 indicated excellent agreement .n = 145; 87.9%), 18 (10.9%) were females, and only two persons did not answer this question (1.2%). The mean age of the participants was 47.3 ± 14.5 years, the mean time experience as sheep farmer was 23.5 ± 19.9 years. More than half of the farms had technical advice, either from the government  or private . In addition, most of the farms  were visited by a veterinarian, at least once a year. The majority of sheep farmers were males  on lambs at a mean age of 3.8 ± 3.1 months (range= 0.5–18). The most common method for identification was ear notching (52.1%), followed by the use of an ear tag (39.4%), and both methods (8.5%). The totality of farmers (100%) considered identifying their animals as a sign of property and because the procedure allowed them to maintain accurate records. No farmers reported the use of analgesics (100%) during this procedure, which is mainly performed by themselves (33.9%) or by farm personnel (26.3%). Nonetheless, 33% of farmers did not answer the question of who performed the procedure.Of the sheep farmers, 91.2% answered that they dock lamb tails at a mean age of 3.4 ± 1.9 months (range = 1–12). The most common method reported was the use of a knife (67.3%), followed by a rubber ring (18.7%), and hot iron (11.2%). Reasons for tail docking included: To improve mating (29.1%), to improve animal sanitary conditions (22.3%), for easier handling (21.4%), esthetic reasons (10.7%), and to improve reproduction (6.8%); 3.9% of farmers answered that they do not know why they do it. Other less frequent answers (< 2%) included: Tradition, to improve animal productivity, and as a method of animal identification. The majority of sheep farmers (85.9%) do not use analgesics during this husbandry practice. Similar to animal identification, tail docking is performed by sheep farmers (40.6%) and by farm personnel (29.7%).The majority of sheep farmers (49.7%) performed tail docking only in animals that will remain in the farm for breeding purposes, 25.9% only dock ewe lambs and 10.9% perform it on male and ewe lambs. Most farmers (55.9%) leave a short tail stump that does not cover the tip of the vulva in ewe lambs and at a similar length in male lambs; 33.6% dock lambs’ tails at a medium length and 0.8% at a long length. The main reasons given by farmers in relation to why they dock lambs’ tails at these lengths were: To improve mating (33.8%), to improve animal sanitary conditions (18.4%), tradition (12.2%), esthetic reasons (10.2%), and to protect the perineum (5.4%).Regarding castration, 52.3% of the sheep farmers castrate their male lambs at a mean age of 7.6 ± 15 months (range = 1–72). The most common method used was a rubber ring (75.5%), 14% use a knife, and 10.5% use other methods. The main reasons for castrating their lambs included: Handling (48%), commercial requirements (20%), improvement of animal productivity (17%), and to avoid breeding (15%). Most of the farmers (92.6%) indicated that they do not use analgesics during castration with the procedure mainly being performed by farm personnel (40.8%) or by themselves (32.4%).The majority of sheep farmers  had previously heard about animal welfare. Regarding animal welfare , 83.6% oThe importance that sheep farmers give to animal welfare was mainly associated with the facts that it affects animal health and production (53.55%), it influences meat quality (28.9%), and it is a consumer requirement (12%). A 5.6% of participants answered that animal welfare was something indifferent to them. The most important animal welfare concerns reported by sheep farmers on their farms, in order of importance, included: Nutrition and water availability > diseases > predators. In general, sheep farmers perceived that animal welfare in their farms was good (65.7%) and regular (32.8%), with a minority perceiving that it was bad (1.5%).p < 0.05). The most frequent pain scores for castration and tail docking were 10 (mode 10), with 55.2% and 35.2% of sheep farmers scoring both procedures with a score of 10, respectively (p < 0.05). Hoof trimming, shearing, and deworming were scored with the lowest pain scores in the NRS . Only data from farmers that completed all the NRS (seven husbandry practices) was included. Husbandry practices that farmers scored as the most painful procedures included both castration and tail docking . Castratectively . As show the NRS .p < 0.05) between castration and tail docking pain scores and between tail docking and identification pain scores , regardless of the method used for these procedures. In contrast, castration and identification pain scores were not correlated . Tail docking and animal identification using a knife were scored with higher scores (p < 0.05) by sheep farmers, compared to when these procedures were performed using a rubber ring and ear tagging, respectively (p > 0.05) between tail docking with a knife and hot iron, nor between castration methods (p > 0.05).A positive and significant correlation was obtained  their pain perception only for deworming and vaccinating (p < 0.05) of farmers’ educational level on pain perception associated with the majority of husbandry practices (p < 0.05). The time experience as sheep farmers (p < 0.05) only affected the farmers’ pain perception for vaccinating. Flock size and farm size were factors that affected (p < 0.05) farmers’ pain perception associated with hoof trimming, deworming, and vaccinating. Farmers who had larger flocks and farms gave lower pain scores (p < 0.05) for these husbandry practices than farmers who had smaller flocks and farms . Nonetheless, for the “Severe pain” category, the agreement among farmers was fair to good  and more than 500 ewes (30.3%). Sheep farmers in the Chilean Patagonia  have large flocks (more than 500 ewes) in extensive production systems and large farms . In contrast, sheep farmers of central Chile (Region of Libertador Bernardo O’Higgins) are characterized by small flocks (less than 50 ewes) and less than 50 hectares. These differences between regions in terms of flock and farm size are in agreement with Gallo et al. [Current European and Chilean legislation ,10,19 maThe most frequent method used by sheep farmers to carry out identification and tail docking in lambs was the knife. Several authors ,34,35 haAnimal identification and castration are performed mainly for handling and commercial reasons, while there are several reasons why sheep farmers carry out tail docking, including improving both mating and farm sanitary conditions. According to the scientific evidence, these arguments are questionable because we are not aware of any studies reporting that tail docking improves mating ,39. MoreSheep farmers mostly performed tail docking to ewe lambs and to those ewe and male lambs that remain in the farms for breeding purposes. This fact could also be associated with the main reason why they perform this practice: To improve mating. Similarly, the length at which farmers tail docked their animals may be also influenced by this factor, mainly due to the fact that the vast majority of sheep farmers carry out this procedure, leaving a short tail that does not cover the tip of the vulva. However, it has been demonstrated that short-tail docked animals have a greater incidence of rectal prolapses and even a higher risk of flystrike ,41. TherWhen painful husbandry practices cannot be performed at an early age, legislation mandates for the use of analgesics ,20. ThisThe majority of sheep farmers have previously heard about animal welfare and they associate it to physical, mental, and behavioral conditions of animals. Also, more than half of the surveyed farmers agreed that animal welfare is important to ensure animal health and production. These findings are in agreement with those reported by Australian  and BrazCastration and tail docking were the only husbandry practices in which the totality of sheep farmers agreed as inducing pain . These rCastration was perceived by sheep farmers as the most painful husbandry practice for lambs, followed by tail docking and animal identification , which iThe pain perception differences found between sheep farmers in relation to the method used to perform certain husbandry practices  are in aFlock size and farm size influenced the perception of pain . Sheep fThe majority of sheep farmers perceived hoof trimming, deworming, vaccinating, and shearing as painless practices, although a high variability in their pain perception was observed . This vaThe poor degree of agreement observed among sheep farmers for pain recognition associated to husbandry practices  could beThis study may be limited by the selection of farmers that attended an industry-government continuing education event and a self-administered survey format. This process may have selected from a cohort of more generally literate farmers than the population targeted. This is the first study in Chile that approaches how sheep husbandry practices are performed and the farmers’ perception of pain and animal welfare; further studies on the subject should follow.Animal identification, tail docking, and castration are painful husbandry practices that are carried out by Chilean sheep farmers at later ages than recommended by the international literature, using methods that may have a negative impact on animal welfare and analgesia is rarely used. Sheep farmers perceived castration and tail docking as the most painful husbandry practices, however, they carried out these procedures with methods that they perceived as the most painful for animals. The vast majority of sheep farmers perceived a regular to good level of animal welfare in their farms, possibly due to the fact that they considered other animal welfare issues as more important than husbandry practices, such as animal nutrition and water availability. Although there was a poor global agreement among sheep farmers in the recognition of pain associated to routine husbandry practices, there was a good level of agreement for the most painful husbandry practices, such as castration and tail docking, notwithstanding that no analgesia is used for both procedures. It is inferred that the lack of use of analgesics would not be explained by sheep farmers’ pain perceptions nor their time experience as sheep farmers, but other important factors that influence this decision would exist, such as practical and economic reasons, as well as the fact that in extensive pasture production systems, sheep farmers do not have a close human:animal interaction and there are difficulties to actually observe animals.


Chromatograms have been recorded at 280, 310 and 370 nm to gain information on the composition of benzoic acids, hidroxycinnamic acids and flavonoids, respectively. HPLC-UV/vis data consisting of compositional profiles of relevant analytes has been exploited to characterize cava wines produced from different base wine blends using chemometrics. Other oenological variables, such as vintage, aging or malolatic fermentation, have been fixed over all the samples to avoid their influence on the description. Principal component analysis and other statistic methods have been used to extract of the underlying information, providing an excellent discrimination of samples according to grape varieties and coupages.Cava is a sparkling wine obtained by a secondary fermentation in its own bottle. Grape skin contains several compounds, such as polyphenols, which act like natural protectors and provide flavor and color to the wines. In this paper, a previously optimized method based on reversed phase high performance liquid chromatography (HPLC) with ultraviolet/visible (UV/Vis) detection has been applied to determine polyphenols in cava wines. Compounds have been separated in a C Cava is a sparkling wine of high quality with Protected Designation of Origin (PDO) produced by the Champenoise method based on the second fermentation and aging period in its own bottle ,2. Cava Red wines from black skin grapes are much richer in polyphenols than rosé or white wines due to the simple fact that these compounds are extracted from the skin grape during the maceration process. Furthermore, cavas produced from the Chardonnay variety have a high content in polyphenolic substances; however, they differ in composition from those found in the varieties of black grapes . MoreoveIt has been pointed out elsewhere that naturally occurring components of food product may serve as efficient descriptors of some food features such as origin, varietal constituents, manufacturing practices, etc. ,9. AmongIn the field of cava and wines, polyphenolic data can be correlated with factors  with a high impact on quality and economic issues, such as the qualification with the PDO or the final price . Some ap18 column under an elution program based on increasing the percentage of methanol. In order to focus on varietal and blending issues more specifically, samples under study were chosen homogeneously regarding some winemaking factors such as vintage, aging in cellars and application of malolactic fermentation. As a result, descriptors of wine blending could be investigated without the influence of other disturbances. PCA was applied to compositional data to carry out an exploratory study of the coupages made with different grape varieties. Despite the lack of selective descriptors, results showed that various polyphenols were more abundant in some coupages. As a result, we concluded that the proposed method resulted in a simple, fast and suitable approach towards the characterization and classification of cavas according to base wine varieties and blends using polyphenolic concentrations as the source of information.In this paper, relevant polyphenols were determined in different cava samples by an HPLC-UV/vis method previously established . Compounp- coumaric acids, (+)-catechin, (−)-epicatechin, ethyl gallate, resveratrol, rutin, myricetin and quercetin. These compounds were selected according to their abundance and relevance in white and rosé cavas. Stock standard solutions of 1000 mg L−1 of each polyphenol were prepared in MeOH. Working standard solutions consisting of a mixture of analytes at concentrations ranging from 20 to 0.05 mg L−1 were prepared in MeOH:water .The mobile phase was prepared with formic acid , methanol  and water . Polyphenols of analytical quality were purchased from Sigma-Aldrich : gallic, homogentisic, protocatechuic, caftaric, gentisic, vanillic, caffeic, syringic, ferulic, −1 sugar), 61 brut (3–12 g L−1), 13 dry (17–32 g L−1) and 12 semi-dry (32–50 g L−1).Cava samples under analysis were kindly provided by the winery Codorníu Raventós S.A. Cavas were produced from base wines of Penedès and Costers del Segre regions . Samples consisted of white and rosé cavas all of them of 2015 vintage and aged for a period of 18 months, elaborated from various coupages of base wines of Chardonnay, Macabeu, Xarel·lo, Parellada, Pinot Noir, black Garnacha and Trepat varieties as follows: Coupage C (58 samples) corresponded to the three classical varieties: Macabeu, Xarel·lo and Parellada; Coupage E (six samples) was based on the classical combination with a small percentage of Chardonnay; Coupage A (15 samples) consisted of Chardonnay (70%) plus Macabeu, Xarel·lo and Parellada blending (30%); Coupage G (three samples) was Chardonnay 100%; Coupage S (two samples) was Chardonnay (50%) plus Macabeu and Xarel·lo (50%); Coupage T (four samples) corresponded to a rosé cava from Chardonnay (30%) and Pinot Noir (70%); Coupage V (nine samples) corresponded to a rosé cava from Pinot Noir, black Garnacha and Trepat. For each coupage, samples were independently produced  and bottled. From the point of view of sugar content, the set comprised 11 brut nature (<3 g LSamples were degasified and filtered through a nylon membrane (0.45 µm pore size) prior HPLC-UV/vis analysis. A quality control solution consisting of mixture of 50 µL of each cava sample was prepared to evaluate the reproducibility of the chromatographic method and the significance of the descriptive PCA models. Cava samples were analyzed randomly and the quality control was injected every 10 samples.The chromatographic system consisted of an Agilent Series 1100 HPLC Chromatograph  equipped with a quaternary pump (G1311A), a degasser (G1322A), an automatic injection system (G1392A) and a diode array detector (G1315B). An Agilent ChemStation for LC 3D (Rev. A. 10.02) software was used for instrument control and data processing.18 column . 0.1% of formic acid in water (v/v) (solvent A) and methanol (solvent B) were used to create the following elution gradient and the flow rate was 1 mL min−1: from 0 to 20 min, B(%) 15–60 lineal increase; from 20 to 22 min, B(%) 60–90 lineal increase; from 22 to 27 min, B(%) 90 isocratic range, cleaning step; from 27 to 27.5 min, B(%) 90–15; from 27.5 to 30, B(%) 15 isocratic range, conditioning step. The injection volume was 10 µL. Chromatograms were acquired at 280, 310 and 370 nm.The chromatographic method was optimized and validated elsewhere . BrieflyPreliminary statistics on polyphenolic contents in cava samples were gained from box and radial plots obtained with Excel. Exploratory studies by Principal Component Analysis (PCA) were carried out using the PLS-Toolbox working with MATLAB. The data matrix to be treated consisted of concentration values of quantified polyphenols in the cava samples. Data was autoscaled to achieve a similar weight to all the polyphenols regardless differences in amplitude and magnitude.Sample patterns related to the different grape varieties and blends and other oenological practices were deduced form the interpretation of the scatter plot of scores of the first principal components (PCs). The distribution of variables on the space of the PC, shown in the plot of loadings, provided information on polyphenol correlations. Compounds up- or down expressed, which could be considered as tentative markers, were assessed from the simultaneous interpretation of scores and loadings graphs.−1 was also included for identification purposes. It can be seen that most of the selected compounds were detected although some differences in the concentration levels were encountered among coupages. Analytes were quantified by linear regression models stablish using 10 standard mixtures with concentrations ranging from 0.05 to 20 mg L−1. Differences in compositional profiles were the basis of further sample discrimination as a function of base wine varieties and coupages.As indicated above, an HPLC-UV/vis method previously developed for the determination of relevant polyphenols in white wines  was here−1, respectively. Gallic, homogentisic and caffeic acids, and catechin occurred at moderate levels, ranging from 1 to 5.5 mg L−1. Other minor components such as epicatechin, protocatechuic acid, p-coumaric acid vanillic acid and syringic acid were found at mean concentrations below 1 mg L−1. Resveratrol and flavonoids, such as rutin and myricetin, were scarce . The descriptive ability of each polyphenol  was accounted from the relative standard deviation of the concentration in the series of cavas. The variability ranges given in The analysis of the 97 samples under study revealed that, in general, hydroxycinnamic acids and their esters with tartaric acid were predominant species in this kind of winemaking process; benzoic acids were also abundant, while flavonoids were scarcer. As detailed in Boxplots and radial diagrams were used to explore the performance of polyphenols to distinguish cava as a function of coupages. −1. This compound was not up- or down-expressed in any particular variety, thus it resulted in a poor descriptor. Additionally, gallic acid was widespread, although its concentration was slightly increased in rosé samples with Pinot Noir and black Garnacha varieties. Syringic acid was important in rosé cavas while it was quite residual in classical and Chardonnay-based cavas (−1) and reached 5- to 10-fold higher concentrations in the other classes  and Chardonnay cavas showed the richest overall polyphenol contents, while the classical bends of Macabeu, Xarel·lo and Parellada cavas contained lower polyphenol levels A. In acced cavas C; a simied cavas D and hom classes E. All thCompositional profiles were used as the data to be treated by PCA for a more comprehensive study focused on identifying potential markers of the different classes of wine varieties and blends. The dataset consisted of concentration values of each polyphenol in the cava samples and the quality controls (QCs). As remarked in the experimental section, QCs were included to assess the soundness of the model. A first exploration carried out on the whole data matrix indicated that samples were reasonably distributed according to the coupages. Some minor components, such as resveratrol, rutin and myricetin, with concentrations close to the detection limits of the method, displayed high variability ; thus, they were excluded from the dataset to obtain a more robust and accurate description of sample behavior. The rest of the variables, which exhibited RSD values lower that 6%, were taken for analysis.The PCA model working with the refined set indicated that circa 75% of the information was captured with three principal components (PCs). In particular, PC1 retained 41.3% of variance, PC2 23.3% and PC3 9.9%. Scatter plots of scores of the relevant PCs revealed interesting patterns on sample distribution. In the case of PC1 versus PC2 A, QCs weInterestingly, PC1 modeled the overall polyphenolic content with the richest cavas on the right and the poorest on the left. PC2 explained differences among rosé, classical and Chardonnay . More specifically, three main trends in the sample distribution were encountered, namely: (i) cavas in the left side were mainly elaborated with classical grape varieties of Macabeu, Xarel·lo and Parellada; (ii) samples in the bottom-right corner corresponded to monovarietal Chardonnay; (iii) rosé cavas prepared with black grapes appeared in the top-right corner. Intermediate situations of coupages elaborated with variable percentages of Chardonnay  were distributed accordingly see A. FinallThe scatter plot of loadings of PC1 versus PC2 B describ−1 for semi-dry to less than 3 g L−1 for brut nature. In the current case, PCA plots showed that the sugar content was uncorrelated with polyphenols and had no influence on the sample distribution. Regarding geographical factors and agricultural practices that may affect substantially the phenolic levels, their effect on the set of samples under study was buffered, since enologists combined appropriate proportions of base wines from different regions to obtain products with more constant physicochemical and sensorial attributes, according to commercial parameters of quality and the costumer’s preferences. Finally, as indicated above, other variables such as vintage and aging, which may notably influence on the polyphenolic composition, were controlled for; thus, patterns observed mainly depended on the combinations of base wines.Apart from the relationship between phenolic matter and grape varieties, the sugar content was another variable of the set of samples under study. The liquor of expedition after bottle disgorging provided different concentrations of sugar, ranging from 50 g LSparkling wines belonging to the protected designation of origin Cava were analyzed with a chromatographic method for the determination of polyphenols. Wines from different coupages were considered including monovarietal Chardonnay samples, rosé samples composed of Pinot Noir, Trepat and black Garnacha, the classical blends of Macabeu, Xarel·lo and Parellada and other mixtures containing variable amounts of Chardonnay. Other oenological variables, such as vintage, aging and malolactic fermentation, were fixed for all the samples so that they did not affect the description. In general, overall polyphenolic contents were higher in Chardonnay wines, followed by rosé, while the classical coupages were circa 20% poorer in these components. Patterns deduced from boxplots and radial diagrams were confirmed from a comprehensive data exploration by principal component analysis. Although no molecule was found to be a specific descriptor of cava classes, syringic acid was more abundant in rosé wines, the hydroxycinnamic acids were up-expressed in Chardonnay samples and vanillic acid was almost residual in red grape products. Summarizing, polyphenolic profiling combined with a chemometric data analysis resulted in an excellent approach to deal with the characterization of cava wines according to the coupages. This strategy could be extended to further classification and authentication purposes.


Plastic pollution is a rapidly worsening environmental problem, especially in oceanic habitats. Environmental pollution with microplastic particles is also causing food consumed by humans to be increasingly polluted, including table salts. Therefore, we present the first study which focuses only on table salt products purchased in Taiwan which we examined for the presence of microplastics. We used Fourier transform infrared spectroscopy to identify the polymer type of each particle. Within 4.4 kg of salt, we detected 43 microplastic particles which averages to 9.77 microplastic particles/kg. The identified polymer types were, in descending abundance, polypropylene, polyethylene, polystyrene, polyester, polyetherimide, polyethylene terephthalate, and polyoxymethylene. We combined our novel results with those of previous studies to provide the first global review of microplastic contamination of table salts. We found that 94% of salt products tested worldwide contained microplastics, with 3 out of 27 polymer types  accounting for the majority of all particles. Averaging over seven separate studies, table salts contain a mean of 140.2 microplastic particles/kg. With a mean annual salt consumption of ~3.75 kg/year, humans therefore annually ingest several hundred microplastic particles from salt alone. Meanwhile, global plastic production and waste generation have been growing exponentially and reached about 335 million tons in 201616. Of these, between 4.8 to 12.7 million metric tons are estimated to enter the oceans annually17.Plastic pollution is a growing environmental problem in terrestrial habitats33. Meso-, micro-, and nanoplastic particles are generally defined to be the following size categories, respectively: <25 mm–5 mm, 5 mm–1 μm, and <1 μm11.Once in the environment, plastic objects and fragments (1) visually and structurally damage coastal, oceanic, and riverine environments, with negative effects on tourism, (2) spread invasive species, (3) damage and endanger ships, (4) cause injury and death of animals through ingestion and entanglement, and (5) eventually degrade to meso-, micro-, and nanoplastic particles which can enter the food chain directly or contaminate it via the leaching of their chemical and often toxic ingredients41; (3) the indirect contamination of air, food, and water with unhealthy substances leached from the plastics into the environment46; (4) and microplastics serving as pathogen vectors47.Potential impacts on human health are: (1) accidents; (2) the direct ingestion of microplastic particles (microplastics) via food and the possible resulting internal injury33, and table salts54. Unlike rock salts which are mined underground and were formed in pre-modern times, human-consumed sea salts and lake salts are usually produced through a crystallization process whereby seawater or brine is evaporated by heat and wind. Therefore, pollutants found in these waters, including microplastics, find their way into the end product, namely table salt. Seven previous studies demonstrated the presence of microplastics in table salts which originated from 29 different countries54.Microplastics have already been found in various human foods: beer, drinking water, honey, seafoods, sugarWhen we began this study in 2017, no study had examined table salts purchased in Taiwan. Therefore, we decided to examine the most commonly available salt products in Taiwan for microplastic contamination. The main purpose was to establish whether a potential health risk from microplastic ingestion was also present in products sold in Taiwan, and what the level of contamination was.et al.54 which included four salt products purchased in Taiwan as part of their global study. While we thus lost the novelty of being the first to test products purchased in Taiwan, our study examined a higher number of products and thus remains the first detailed study focusing only on products purchased in Taiwan.During the course of our study, several other similar studies came out, including Kim Given this recent accumulation of studies examining table salts from around the world, we also decided to conduct a global review of microplastic contamination of table salts in order to present the first global estimates of (1) the percentage of product samples which are contaminated, (2) the mean number of microplastics per kg of salt, (3) the estimated number of microplastics annually consumed by humans, (4) the sizes and shapes of microplastics, and (5) the number and percentages of identified polymer types. This novel and comprehensive review thus presents the first global estimates of microplastic contamination of table salts which are invaluable in order to be able to evaluate the potential health risks from the ingestion of microplastics via salt consumption.A total of 4.4 kg of salt (400 g * 11 products) was dissolved and filtered, and a total of 667 potential microplastic particles were visually detected on the filter papers Table . The othIn most controls, <10 potential microplastic particles were found . Seth and Shriwastav53 detected 69.4 particles/kg but only ~80% of their particles were plastic particles, which yields a final estimate of 69.4 * 0.80 = 55.5 microplastic particles/kg . The other estimates were 1.88 microplastic particles/kg for Karami et al.49, 32.1 microplastic particles/kg for Gündoğdu51, 506 microplastic particles/kg for Kim et al.54, and 9.77 microplastic particles/kg for our study , particularly for particles <500 μm size52. Averaging over the seven included studies yields a mean ± standard deviation (and range) of 140.2 ± 183.9 (1.9–506.0) microplastic particles/kg.Our global review found eight relevant studies55 Table . First, 55 Table . Yang etet al.55 and Renzi and Blašković52, mean sizes were only reported in three out of the seven studies , and films, foams, pellets, and sheets were also identified.Second, we reviewed sizes and shapes. Again excluding Kosuth Third, we reviewed the mean number of polymer types which was 8.1 types across the seven included studies Table . In tota57. Using this method, we found 43 microplastics within 4.4 kg of salt from 11 salt products, or 9.77 microplastic particles/kg  tested worldwide contained microplastics. Such a level of contamination is rather troublesome given that one could be justified to demand that no salt product should be contaminated.Our global review supports the supposition that almost all salt products are now contaminated with at least some microplastics. Our study as well as four other studieset al.49 to the highest by Kim et al.54 . This large amount of variation is also apparent when we mapped the global distribution of the mean number of microplastic particles/kg . We assume that both the variation in the actual contamination of the products  and the variation in the laboratory procedures used in the seven studies explains this rather large variation. We therefore suggest that standard protocols should be agreed upon  and that, at a minimum, researchers should report all the different data types presented in Tables 59.The variation in microplastic particles/kg across the seven studies is surprising as it covers more than two magnitudes from the lowest by Karami et al.60 in their Table 7 list 13 polymer types which were detected in 42 studies which identified microplastics recovered from the marine environment, with polyethylene, polypropylene, and polystyrene detected far more often than the other 10 polymer types. Interestingly, the number of studies in which each respective polymer type was detected in Hidalgo-Ruz et al.’s60 review correlates significantly with the number of studies in which the respective polymer type was detected in our global review .While some of the contamination of the salt products happens during the processing and packaging stages (see Discussion below), it is likely that most contamination is due to environmental contamination. We found some new evidence to support this supposition of environmental contamination being the main culprit. Hidalgo-Ruz et al.48, Gündoğdu51, Kim et al.54, and, to a lesser degree, Karami et al.49 and our study found that the number of microplastics generally followed a trend of sea salts > lake salts > rock/well salts  contamination does not originate from the salts but from sources during the processing and packaging of the productsne Table  and alsone Table . The onene Table  has a pine Table . Therefoet al.49 identified several pigments, and Kim et al.54 identified 2.5% natural polymers and 0.7% mineral particles, while we identified many natural materials such as cotton, minerals, natural leaves, paper, or seeds. We did not dissolve natural materials before filtration (see Methods), as most other studies did, and this different methodology may explain our relatively low percentage of microplastic particles among all potential particles  and several much smaller islands and island groups and has a human population of approximately 23.5 million people. It has the 22Over three weeks in June 2017, we visited 28 different supermarkets in Taipei, Taiwan, and wrote down every edible table salt product which was for sale. We then purchased the 10 most commonly sold sea salt products . We also added one rock salt product for comparison. We assume that these 11 products are all regularly and widely consumed by Taiwanese people. The commercial names of these products cannot be made public for privacy reasons.For each product, we obtained information for packaging, weight per package, origin of salt, and salt type from the original package or the company’s website given on the package (in most cases translated from Chinese) , fragments (jagged and irregular shaped particles which often have an uneven surface), and pellets (rounded particles). All particles were photographed.We examined each filter paper using a Hamlet NSZ-606 Zoom Stereo Microscope (range of magnification 0.8x–5x). We counted the number of potential microplastic particles which we could visibly detect for each filter paper and categorized them into three main groups based on overall shape using the definitions established by Karami −1.We analyzed 667 potential microplastic particles with our FTIR spectroscopy setup described below. Of these particles, 647 particles were analyzed with FTIR spectroscopy while the remaining 20 particles were only measured for size because they did not return usable spectra. In January, February, and June 2018, we used an FTIR microscope  in attenuated total reflectance (ATR) mode, equipped with a liquid nitrogen-cooled mercury cadmium telluride (MCT) detector at the Korean Institute of Ocean Science and Technology for our spectroscopy measurements. We used a diamond tip, and the spectra were recorded as 64 scans in the spectral range of 650–4000 cm68. To measure the size of each particle, a ruler built into the measuring program of the FTIR microscope was adjusted to measure the maximum dimension of the particle. This equipment has been used for various studies of microplastics before .Plastic polymer types were identified by matching the sample’s IR spectrum with the spectra stored in an FTIR polymer spectrum library which is already integrated into the measuring program of the FTIR microscopeThe results of our spectroscopy analyses are available in the Supplementary Appendix S1.55 which we found, we also checked (1) the references cited within that study, and (2) the references which cited that study (both in Google Scholar and Web of Science). To add missing information to our global review, we also contacted the corresponding authors of each study to obtain additional information, with several replying (see Acknowledgements).In October 2018, we used Google Scholar and Web of Science to obtain all previous studies which investigated the presence of microplastic particles in salt products. We used combinations of obvious keywords  to find studies whose title and abstract we then checked for relevance. For each of the eight studieset al.48, Gündoğdu51, and Seth and Shriwastav53 used a one-way analysis of variance (ANOVA) to test for mean differences of the abundance of microplastics among salt types or products while Kim et al.54 used a Mann-Whitney U-test. However, we a priori refrained from doing so because of (1) our small sample sizes for one of the two salt types  and (1) the small sample sizes of plastic particles which we detected in each product (1–8 particles, Table 55 also refrained from the use of any statistical tests. Instead, we used standard descriptive statistics, such as the mean, standard deviation, range, and percentage, to describe the results of our laboratory work. For the global review, we used the non-parametric Spearman rank correlation because the data were not normally distributed.Yang Microplastic contamination of table salts from Taiwan, including a global reviewPlastic pollution is a rapidly worsening environmental problem, especially in oceanic habitats. Environmental pollution


Danio rerio) have recently emerged as a valuable laboratory species in the field of behavioral pharmacology, where they afford rapid and precise high-throughput drug screening. Although the behavioral repertoire of this species manifests along three-dimensional (3D), most of the efforts in behavioral pharmacology rely on two-dimensional (2D) projections acquired from a single overhead or front camera. We recently showed that, compared to a 3D scoring approach, 2D analyses could lead to inaccurate claims regarding individual and social behavior of drug-free experimental subjects. Here, we examined whether this conclusion extended to the field of behavioral pharmacology by phenotyping adult zebrafish, acutely exposed to citalopram  or ethanol , in the novel tank diving test over a 6-min experimental session. We observed that both compounds modulated the time course of general locomotion and anxiety-related profiles, the latter being represented by specific behaviors (erratic movements and freezing) and avoidance of anxiety-eliciting areas of the test tank . We observed that 2D projections of 3D trajectories (ground truth data) may introduce a source of unwanted variation in zebrafish behavioral phenotyping. Predictably, both 2D views underestimate absolute levels of general locomotion. Additionally, while data obtained from a camera positioned on top of the experimental tank are similar to those obtained from a 3D reconstruction, 2D front view data yield false negative findings.Zebrafish ( Preclinical animal models constitute a central tool to detail the fundamental mechanisms underlying the expression of human emotions in physiological and pathological conditions . Within In parallel with the aforementioned evolutionary roots, the underlying biological determinants of anxiety are very well conserved across different taxa. For example, the neuroendocrine machinery activated in response to external stressors exhibits striking homologies and analogies among fish , birds , rodentsAlthough rodents have traditionally constituted the species of choice in this field , zebrafiHigh-throughput behavioral experiments on zebrafish generally share the following methodological structure: administration of experimental treatments , videorecording of observable phenotypes, offline scoring of video, coding of the observed behaviors, and data analysis . TraditiHowever, from the two-dimensional (2D) view offered by a single video-camera it is impossible to phenotype the three-dimensional (3D) swimming pattern exhibited by zebrafish. This consideration prompted the design and development of experimental platforms capable of investigating zebrafish behavior adopting a 3D approach . We receIn the present study, we aimed at prospectively investigating whether 3D scoring of zebrafish behavior may also benefit zebrafish research in the field of behavioral response to psychoactive substances. To this aim, we exposed experimentally naïve zebrafish to drugs capable of modulating anxiety-related behaviors in both humans and zebrafish , and theThe objective of this study was twofold: first, we sought to replicate existing findings indicating that ethanol  and citaDanio rerio), with a 1:1 male/female ratio were used in this study. The fish were purchased from Carolina Biological Supply Co. , and housed in 10 L  vivarium tanks , with a density of no more than 10 fish per tank. Fish were kept under a 12 h light/12 h dark photoperiod  type I error probability a = 0.05 and power 1 − b = 0.80 ; and (iii) minimum difference between control and treatment group means D = 0.17. The sample size resulting from this calculation was 15 subjects per group. To promote the generalizability of our findings, we conducted experiments on both males and females. We thus increased the sample size to 16 per group . We estimated that a sample size of 16 subjects (per group) would have 80% power to detect a 0.60 effect size on the principal outcome measures with a two-tailed significance level of 0.05.The number of fish used in the study—compatible with obtaining sufficiently reliable and biologically relevant data—was estimated through a power analysis. Briefly, we computed the minimum required sample size considering the two-tailed Student  studies : (i) staTo obtain 3D trajectories, we used two Flea 3 USB high resolution cameras , one overhead and one in front. Videos were acquired at 30 frames per second, using a resolution of 588 × 264 pixels from the top view and 652 × 360 pixels from the front view; the typical size of one video from the top view was six to seven gigabytes, while the front view was nine to 10 gigabytes. The dimensions of the test tank were 29 cm (length) × 14 cm (height) × 8.5 cm (width) and water 13 cm deep, similar to tanks used in comparable studies . The disTo maximize the visual contrast and ease automatic tracking, the bottom of the tank was lined with white contact paper. The two short sides of the tank were covered with black contact paper to prevent reflection. On the other hand, the two long sides were kept transparent to allow data acquisition and avoid position bias . The experimental arena was surrounded by black curtains to prevent light reflection and visual disturbance from the outside.Experiments, performed in June 2018, were conducted on seven groups, each consisting of 16 subjects . Specifically, the experimental design entailed one control group exposed to vehicle (water), three groups treated with citalopram , and three groups treated with ethanol . The fish were randomly allocated to each of the seven conditions in the following way. The conditions were randomly distributed over several weeks, testing eight subjects per day (four in the morning and four in the afternoon). We balanced sex across conditions, and conditions across mornings and afternoons. Male and female fish were kept in separate tanks; in total, fish were housed in 12 tanks. At the beginning of each test session, we sampled one subject from a tank. Such a tank was different from that out of which we chose the previous subject tested in the same condition. This procedure guaranteed that potential tank effects were distributed evenly across all experimental groups.Due to technical issues, four trials had to be discarded: this resulted in a slight reduction in the number of subjects in the 100 mg/L citalopram group (15 subjects instead of 16) and in both the 0.25% and 1.00% ethanol groups . Based upon Since these substances required a differential pre-exposure time , we devised a common procedure for vehicle, citalopram, and ethanol. Thus, 1 h before testing, fish were placed in a 500 mL beaker filled with 450 mL of the following fluid: water for control and citalopram groups, or a solution of ethanol  for the other groups. 5 min before testing, an additional 50 mL of fluid were slowly added to the beaker over a period of 20–30 s. These 50 mL were constituted by either water (the control group), an ethanol solution of the concentration already present in the beaker (the ethanol groups), or a concentrated solution of citalopram that resulted in a final concentration in the beaker of 30, 50, or 100 mg/L . Fish were left in the beaker for 5 min, at the end of which they were transferred to the test tank and recorded for 6 min.Simultaneous recording from both cameras was initiated before transferring the fish into the test tank. In addition, at the beginning of the recording, a laser beam, visible from both cameras, was pointed into the test tank in order to ensure later synchronization of both video streams. At the end of the experiment, the fish was hand-netted into a separate tank.All the experiments were performed at the New York University Tandon School of Engineering  in accordance with relevant guidelines and regulations, with National Institutes of Health guide for the care and use of Laboratory animals , and was approved by the University Animal Welfare Committee of New York University under protocol number 13-1424.https://github.com/sach1tb/peregrine). The top and front view cameras provided time series of the trajectory projected onto the x-y and x-z planes, respectively. Each pair of tracks were automatically synchronized using the common x coordinate along length of the tank. The time-series for each x coordinate of the pair were shifted relative to each other and the relative shift producing the smallest difference was selected. Once synchronized, the tracks from the top view and from the front views were combined to construct the x, y, and z coordinates of the trajectory in the three-dimensional space , average acceleration , average peak acceleration , average angular speed (time-computed on the basis of a finite difference approximation of the curvature of fish trajectories), average peak angular speed , and time spent in the top half of the water column. These nine measures were selected from the technical literature on zebrafish behavior in novel tank tests (Reconstructed trajectories were used to quantify the following ethogram: time spent freezing (percentage of time that the fish moved less than two cm anywhere in the tank over a rolling period of 2 s), time spent wall following , average speed , average peak speed  on nine behavioral measures, objectively scored from 3D trajectories . The PCA was aimed at detecting potential correlations among the variables and identifying underlying orthogonal factors associated with independent domains.Only principal components with eigenvalues larger than one were retained in the analysis. For each compound , the loadings were varimax-rotated, and the resulting scores for each principal component were used as dependent variables in a four  × six  × two  repeated measures analysis of variance (ANOVA) for split-plot designs. Testing males and females served the aim to access a heterogeneous experimental population and therefore improve the generalizability of our findings.For citalopram and ethanol treatments, three principal components with eigenvalue larger than one were extracted by the PCA , accountThese three principal components, derived from 3D observations, were used to test the efficacy of ethanol and citalopram in modifying zebrafish behavior. It was not possible to use PCA to compare the different views since the number of variables that construct the principal components in 3D was greater than that in 2D.To investigate whether 2D projections of 3D trajectories may introduce a bias in the predictive validity of behavioral data on each of the nine measures, we conducted a repeated measures ANOVA for split-plot designs. In this analysis, the two general models for citalopram and ethanol were, respectively: three  × four  × six  × two , and three  × four  × six  × two  repeated measures ANOVAs. Similar to the PCA, predictions of the effect of sex were not considered.For all ANOVAs, the distribution of the model residuals was visually inspected to verify that they were close to normality . StatistWhile main effects of the view factor allowed assessing whether absolute values differed depending on the tracking method, significant interactions between view and any other factor suggested that the effects of the latter were moderated by the tracking method. For example, a significant view × treatment interaction would suggest that the effects of a given compound may vary as a function of how the behavior of the animal was scored . Upon detecting a significant interaction, we performed post hoc comparisons, correcting for type-I errors, to detail whether and which pairwise comparisons were significant. Among these comparisons, those contrasting 2D and 3D were germane to the key question of the study.This statistical model allowed testing the hypothesis that 2D views yielded spurious results compared to 3D data: false positive or false negative findings. Specifically, if 2D analysis reported a significant inter-group difference which was not confirmed by 3D data, we recorded such a result as a false positive. Likewise, if 2D analysis failed to identify significant inter-group differences, which were instead detected using the 3D approach, we recorded such a result as a false negative. For example, consider the case in which we identified a significant time bins × view interaction where animals decreased their average speed as a function of time from 3D data while maintaining their initial average speed throughout the trial from both the 2D views. In this case, had post hoc comparisons between 3D data and any of the 2D data confirmed ANOVA results, we would register a false negative prediction for each of the 2D data. Instead, a false positive finding would be registered in the opposite situation where 3D data indicated the lack of a habituation profile in the average speed, while 2D data would support the opposite.F3,55 = 0.52, P = 0.668) , regardless of the specific experimental group . Absolute values of behavioral anxiety did not significantly vary across citalopram conditions  , albeit at a different rate depending on the citalopram dose . Specifically, while behavioral anxiety remained constant throughout the experimental session in citalopram 50 and 100 mg/L conditions, it significantly declined in control and citalopram 30 mg/L conditions . While positional anxiety did not significantly vary across citalopram conditions  . Such time-dependent profile varied depending on the experimental treatment . Thus, while it remained constant in control and citalopram 100 mg/L, it was low at the beginning of the test session and steadily increased in citalopram 30 mg/L and citalopram 50 mg/L subjects .When analyzing the three components identified by PCA, scored from 3D trajectories, we observed that absolute levels of locomotion were indistinguishable between control and citalopram-treated subjects (condition: = 0.668) . Additio= 0.524) . Yet, it= 0.421) , it signF3,52 = 2.43, P = 0.072) , and that such a decrease was indistinguishable across all experimental groups . Behavioral anxiety did not significantly vary across ethanol conditions  . However, we observed that the habituation profile varied depending on the specific experimental group . Specifically, while behavioral anxiety remained constant in most experimental groups, it significantly declined over time in the ethanol 0.5% condition . Finally, positional anxiety failed to reach a statistically significant variation across ethanol conditions  . Specifically, it significantly decreased for the ethanol 1.0% condition .In response to ethanol administration, absolute levels of locomotion failed to reach a statistically significant variation across experimental groups (condition: = 0.072) . When an= 0.500) , neither= 0.071) , althougHerein, we report data concerning the effects of the views on all the experimental variables measured in the study. For the sake of clarity, in this section, we only report statistical findings associated with the scoring view  and its interactions with time or condition. Results concerning the main effects of condition, time, and their interaction irrespective of view are available in the Before delving into detailed comparisons between the three different views for all the considered behavioral measures, we present an aggregated assessment of potentially inaccurate conclusions that would be drawn from 2D projections against 3D trajectories. The rate of false negative (erroneous reporting of absence of differences in lieu of significant findings in 3D) and false positive (erroneous reporting of significant differences in lieu of non-significantly different findings in 3D) findings is synoptically reported in Average speed: Predictably, average speed varied significantly depending on which view was used to compute it  ; additionally, 2D front view resulted in reduced average speed compared to top view . Experimental subjects did not show a habituation profile to the test, yet 2D top projections indicated that the average speed decreased from the first to the last minute .< 0.001) . SpecifiAverage peak speed: Average peak speed was significantly underestimated in both 2D front and top views in comparison with 3D data  .< 0.001) . While t< 0.001) , experimAverage angular speed: Average angular speed was underestimated in the 2D front view compared to both 3D and 2D top views  .< 0.001) . FurtherAverage peak angular speed: Average peak angular speed was underestimated when scored from 2D top view  .= 0.001) . A decreAverage acceleration: Average acceleration was underestimated in both front and top 2D views compared to 3D ; additionally, 2D front view underestimated average acceleration compared to 2D top view . Average acceleration varied over time depending on the view adopted to score fish behavior  , such a decline was observable also from 2D top view , but only during the last minute in 2D front view .< 0.001) . SpecifiAverage peak acceleration: Average peak acceleration significantly decreased over time, regardless of the specific view adopted to compute this measure  . Additionally, 2D front view yielded a lower average peak acceleration compared to top view .< 0.008) . Yet, avWall following: Time spent wall following was significantly underestimated in 2D front view compared to 3D data . Additionally, this metric was lower in 2D front view compared to 2D top view  , 2D front view data failed to identify this time dependent pattern of thigmotaxis.< 0.001) . While 3Position in the water column : Since this metric takes into account only the vertical position of the fish, it cannot be scored from 2D top view and there is no difference between values from 2D front view and 3D reconstructed trajectories  , post hoc comparisons did not indicate any specific difference.= 0.018) , post hoAverage speed: The different scoring views resulted in variable average speed values  . Additionally, average speed was lower in 2D front view compared to top view . While data inspection suggested that habituation profiles were skewed by the view adopted to score individual behavior , post hoc analyses failed to show significant view-dependent variations in this parameter.< 0.001) . Both 2DAverage peak speed: Average peak speed varied in all subjects, and this profile was apparently influenced by the view adopted to score individual trajectories . This variation was manifested as a robust decline in subjects treated with ethanol 0.50% concentration  . Although ANOVA reported a significant interaction between view and condition , post hoc tests failed to reveal any significant pairwise difference.< 0.001) , as wellAverage angular speed: Predictably, both 2D front and top views underestimated average angular speed compared to the 3D view . Additionally, average angular speed was smaller in 2D front view compared to 2D top view  .< 0.010) . FurtherAverage peak angular speed: Although average peak angular speed appeared significantly different depending on which view was used , such a difference failed to emerge in post hoc comparisons. From all views, average peak angular speed declined throughout the experimental session  (< 0.035) .Average acceleration: In line with most of the locomotion-related variables, average acceleration was underestimated in both 2D front and top views compared to 3D view . Individual habituation profile was differentially expressed by experimental subjects depending on the specific view . Specifically, while 3D and 2D top view data indicated a general decrease in average acceleration throughout the experimental session , such a profile was not visible in 2D front view, showing only a reduction during the third minute of the test . Data analysis suggested that the habituation profile varied depending on both the view and the ethanol treatment . Specifically, we observed that the reduction in average acceleration was significant in ethanol 0.5% , and that this decrease occurred regardless of the specific view from which data were scored.Average peak acceleration: Average peak acceleration varied depending on the specific scoring view ; specifically it was underestimated in both 2D front and top views compared to 3D  and was also less in 2D front view compared to 2D top view  , post hoc tests did not support this suggestion. Thus, acceleration decreased with time in experimental subjects regardless of the specific view adopted.= 0.003) . FurtherWall following: The time spent in the proximity of the walls significantly varied depending on the specific view used to compute it . Wall following was significantly underestimated in both 2D front and top views compared to 3D , and this parameter was less in 2D front view compared to 2D top . The individual habituation profiles varied depending on the view  . While wall following was apparently differed between conditions depending on the scoring view , such a difference was not statistically significant in pairwise comparisons.= 0.001) . SpecifiFreezing: While the time spent freezing seemed to vary depending on the specific scoring view  (= 0.006) , such a The methodological nature of the present study first reverberated in the systematic evaluation of the correlation among the variables that constitute the ethogram exhibited in the novel tank diving test. The PCA revealed the presence of three orthogonal factors, reflecting general locomotion , anxiety-related behavioral patterns , and anxiety-related spatial preference . The first principal component relates to the translational motion within the water tank. The behavioral patterns loading on the second principal component have been consistently associated with anxiety, in the form of erratic movements (zig-zagging) and freezing . From thWhile this analysis aligns with previous evidence indicating that anxiety can be expressed through different modalities, it also points at potential pitfalls of common practice in the construction of the ethogram of the novel tank diving test from 2D views. Specifically, the fact that variables contributing to the same principal component require different perspectives further corroborates the need for a 3D approach. For example, while position in the water column requires a front camera, wall distance and erratic movements need an overhead camera.The analysis conducted on the aforementioned principal components revealed that both citalopram and ethanol influenced anxiety-related behaviors, thus corroborating the predictive validity of the novel tank diving test. Importantly, while citalopram concentration-dependently reduced locomotion and predictably reduced anxiety, ethanol resulted in increased anxiety, but only at a medium concentration . Higher It is worth noticing that, when analyzing discrete parameters rather than focusing on the principal components, some anxiety-related behavioral parameters seemed unaffected by the anxiolytic treatments applied. Specifically, we failed to observe a significant effect of citalopram on the time spent in the upper portion of the tank, a classical measure of anxiety. We note that such absence of a concentration-dependent behavioral response to anxiolytic compounds has also been reported in other studies. The anxiolytic effects of citalopram are likely related to its direct influence on serotonergic concentrations. For example, handling stress has been shown to increase anxiety-like behavior and reduce brain concentrations of the serotonin metabolite 5-Hydroxyindoleacetic acid (5-HIAA) . FurtherWith respect to ethanol, available literature  indicateFurther, since ethanol influences general locomotion, some of its effects on anxiety may be spurious and potentially relate to locomotor effects. For example, a lack of vertical exploration may reflect a decrease in swimming behavior due to the sedative effect of high concentration of ethanol, rather than an anxiety response . In our In order to compare 3D and 2D approaches, all experimental variables were also analyzed independently from one-another. This comparison was aimed at confirming the intuition that locomotion is underestimated when scoring the behavior in 2D and at assessing whether 2D views yielded incorrect conclusions regarding the effects of anxiolytics on individual behavior. Working with raw experimental variables rather than aggregated principal components allowed for a direct comparison of our findings with available literature, where the selected metrics are routinely assessed in pharmacological phenotyping of zebrafish . With reThe core objective of the present study was to evaluate whether 2D views may result in inaccurate rejection of null hypotheses or acceptance of alternative ones. We observed that the specific view consistently skewed the time course of the behavioral response to the novel tank. This was reflected in the presence of ubiquitous significant view × time bins interactions across most of the variables, and only few instances of view × condition interactions. Thus, these data could preliminarily suggest a relative robustness of current scoring methods in zebrafish pharmacology. Yet, in the light of the paucity of drug-dependent effects and of the nature of the statistical model required to test the suitability of the 2D approaches compared to 3D, we argue that this assessment only reflects a partial consideration of the observed results.In our previous study, we demonstrated that 2D experiments are underpowered compared to 3D and therefore more prone to false negative findings than false positive ones . While iThese considerations extrapolate to zebrafish pharmacology, whereby our and others’ data  indicateIn the present study, we examined whether the effort required to analyze zebrafish behavioral response to psychoactive compounds in 3D is warranted or a simpler 2D approach should be preferred. Such an effort resides in the use of two, rather than one, cameras and in the increased computational burden required when dealing with multiple views. The former aspect adds some limited costs to the experiment and requires some design considerations to ensure that the experimental tank is visually accessible from multiple locations. The latter aspect entails an increased workload by the experimenter to manually repair tracks that the behavioral scoring software had flagged during the automated analysis, as well as limited computational costs associated with synchronizing and fusing multiple videos.Although not conclusive, our data suggest that 2D analysis may produce spurious predictions that might confound the generalizability of experimental results. We acknowledge that our claims are specific to an experimental tank which allowed fish to freely swim along the water column. Testing animals in shallow water, restricting behavioral patterns to 2D, may not constitute an ideal solution, whereby it would add an unwanted source of distress to the animals that could mask the effect of the pharmacological manipulation. We also acknowledge that the experimental tank had a length threefold the width, which might have skewed the behavioral patterns of the animals with respect locomotion in the horizontal plane.Finally, it is important to emphasize that in the present study we primarily focused on anxiolytic drugs and we thus cannot extrapolate our findings to the entire spectrum of anxiety-related behaviors. Future studies are needed to test whether the considerations outlined in this study also extend to anxiogenic compounds  and non-pharmacological anxiety-eliciting stimuli .10.7717/peerj.7893/supp-1Supplemental Information 1Click here for additional data file.