Ecology

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Our findings illustrate both the range and extent of wildlife exploitation in Wuhan markets, prior to new trading bans linked to the COVID-19 outbreak, along with the poor conditions under which these animals were kept prior to sale. Circumstantially, the absence of pangolins (and bats, not typically eaten in Central China; media footage generally depicts Indonesia) from our comprehensive survey data corroborates that pangolins are unlikely implicated as spill-over hosts in the COVID-19 outbreak. This is unsurprising because live pangolin trading has largely ceased in China13.We should therefore not be complacent, because the original source of COVID-19 does not seem to have been established. This is doubly important because false attribution can lead to extreme and irresponsible animal persecution. For instance, civets were killed en masse following the SARS-CoV outbreak5, and any unwarranted vilification or persecution of pangolins and bats in relation to COVID-19 would risk undermining otherwise very successful efforts to better protect and conserve wildlife in China.Regarding our insights into broader IWT issues in Wuhan, the animals sold were relatively expensive, representing luxury food items, not cheap bushmeat (Table 1). We thus make an ethical distinction here between the subsistence consumption of bush meat in poorer nations, versus the sort of cachet attached to wild animal consumption in parts of the developed world, notably China14, but also Japan15. While c. 30% of mammals were clearly wild-caught, indicated by trapping and shooting wounds, the captive breeding of other species is commonplace in China. Raccoon dog fur farming is legal in China; however, due to a drop in fur prices, raccoon dogs are now frequently sold off in live animal markets, augmented by wild-caught individuals. Similarly, all American mink (Neovison vison) originated from fur farms—noting that SARS-CoV-2 has been reported in mink farms in Europe and North America16, 17. In contrast, the captive breeding and sale of Siberian weasels (Mustela sibirica), is totally illegal in China, yet they are easy to breed, and sold openly, without attracting law enforcement. Indeed, prior to COVID-19 reforms, although enforcement officers from the Wuhan Forestry Bureau issued permits to market vendors, they were broadly disinterested in what species were sold. Furthermore, although animals were required to have an origin certificate and be quarantined to ensure they did not exhibit overt disease symptoms, no clear policy was enforced on these conditions. This is important because the species that were traded are capable of hosting a wide range of infectious zoonotic diseases or disease-baring parasites (Supplementary Table S1), aside from COVID-19. These range from potentially lethal viruses, for example, rabies, SFTS, H5N1, to common bacterial infections that, nevertheless, represent a risk to human health (e.g., Streptococcus). Indeed, globally, wildlife is thought to be the source of at least 70% of all emerging diseases18.Legislative reform is also vital to clarify unequivocally which species are considered ‘wild’ and cannot be traded legally and safely. Another problem, as encountered by the WHO report is that, retrospectively, it proved difficult to ascertain which species were on sale, even to the genus level, relying solely on the responsible market authority’s official sales records and disclosures1. As we19, 20, and others21, have proposed previously, China’s LFSSP and LESS must be updated to apply proper binomials, and to align with recent taxonomic revisions; for instance, cobra snakes (Nada atra) can be farmed legally for food with permits, but wild caught species, such as water snakes and wolf snakes were also sold in Wuhan, labelled simply as ‘snakes’. Such an application of clear species names would allow for more effective prosecutions19. Furthermore, the WHO reports that market authorities claimed all live and frozen animals sold in the Huanan market were acquired from farms officially licensed for breeding and quarantine, and as such no illegal wildlife trade was identified1. In reality, however, because China has no regulatory authority regulating animal trading conducted by small-scale vendors or individuals it is impossible to make this determination1, 21. Similar discrepancies concerning species identification and origins afflict investigations around the world22.Another important animal trade that requires attention, outside of exploitation as food, is the supply of pets, like the squirrels and crested myna birds sold in Wuhan’s market. Our previous research found annual trade volumes equivalent to c. 17,000 parrots and c. 160,000 turtles (many turtles being invasive if escaping to the wild) sold online as pets via Taobao.com between 2016–2017, in contravention of China’s WACL and/or the Animal Epidemic Prevention Law23,24,25. While not currently the vector of any major viral epidemics, it would be naive to imagine that unconventional pets do not still also pose a serious concern for public health26. This potential for disease is likely exacerbated by poor sanitary and welfare conditions (Fig. 2). More

Sample collection and incubationThree replicates of soil samples were collected from the top 10 cm in of plant-free patches in four ecosystems along the C. Hart Merriam elevation gradient in Northern Arizona25 beginning at high desert grassland (1760 m), and followed at higher elevations by piñon-pine juniper woodland (2020 m), ponderosa pine forest (2344 m), and mixed conifer forest (2620 m). Soils were air-dried for 24 h at room temperature, homogenized, and passed through a 2 mm sieve before being stored at 4 °C for another 24 h. Soil incubations were performed on soils with mass of 20 g of dry soil for measurements of CO2 and microbial biomass carbon (MBC), while 2 g of dry soil aliquots were incubated separately (but under equivalent conditions) for quantitative stable isotope probing (qSIP). We applied three treatments to these soils through the addition of water (up to 70% water-holding capacity): water alone (control), with glucose (C treatment; 1000 µg C g−1 dry soil), or with glucose and nitrogen (C + N treatment; [NH4]2SO4 at 100 µg N g−1 dry soil). All samples for qSIP were incubated with 18O-enriched water (97 atom%) and matching controls necessary to calculate the change in 18O enrichment across the microbial community. We applied water at natural abundance (i.e., no 18O-enriched water) to the larger soil samples prepared for measurement of carbon flux. All soils were incubated in the dark for one week. Following incubation, soils were frozen at −80 °C for 1 week prior to DNA extraction.Soil, CO2, and microbial biomass measurementsWe analyzed headspace gas of soils for CO2 concentration and δ13CO2 three times during the week-long incubation using a LI-Cor 6262 (LI-Cor Biosciences Inc. Lincoln, NE, USA) and a Picarro G2201 (Picarro Inc., Sunnyvale, CA, USA), respectively. Prior to incubation we analyzed soil MBC using the chloroform-fumigation extraction method on 10 g of soil. One sub-sample was immediately extracted with 25 ml of a 0.05 M K2SO4 solution, while a second sub-sample was first fumigated with chloroform (for 5 days), after which it was similarly extracted. Following K2SO4 addition, we agitated soils for 1 h, filtered the extract through a Whatman #3 filter paper, and dried the filtered solution (60 °C, 4 days). Salts with extracted C were ground and analyzed for total C using an elemental analyzer coupled to a mass spectrometer. MBC was calculated as the difference between the fumigated and immediately extracted samples’ soil C using an extraction efficiency of 0.45 (as per Liu et al.26).Quantitative stable isotope probingWe performed DNA extraction and 16S amplicon sequencing on 18O-incubated qSIP soils11,12,13. The procedures targeted the V4 region of the 16S gene as specified by the Earth Microbiome Project (EMP, http://www.earthmicrobiome.org) standard protocols27,28. We used PowerSoil DNA extraction kits following manufacture instructions to isolate DNA from soil (MoBio laboratories, Carlsbad, CA, USA). We quantified extracted DNA using the Qubit dsDNA High-Sensitivity assay kit and a Qubit 2.0 Fluorometer (Invitrogen, Eugene, OR, USA). To quantify the degree of 18O isotope incorporation into bacterial DNA, we performed density fractionation and sequenced 15–18 fractions separately following methods modified from the canonical publication7. We added 1 µg of DNA to 2.6 mL of saturated CsCl solution in combination with a gradient buffer (200 mM Tris, 200 mM KCL, 2 mM EDTA) in a 3.3 mL OptiSeal ultracentrifuge tube (Beckman Coulter, Fullerton, CA, USA). The solution was centrifuged to produce a gradient of increasingly labeled (heavier) DNA in an Optima Max bench top ultracentrifuge (Beckman Coulter, Brea, CA, USA) with a Beckman TLN-100 rotor (127,000 × g for 72 h) at 18 °C. We separated each sample from the continuous gradient into approximately 20 fractions (150 µL) using a modified fraction recovery system (Beckman Coulter). We then measured the density of each separate fraction with a Reichart AR200 digital refractometer (Reichert Analytical Instruments, Depew, NY, USA) and retained fractions with densities between 1.640 and 1.735 g cm−3. We cleaned and purified DNA in these fractions using isopropanol precipitation, quantified DNA using the Quant-IT PicoGreen dsDNA assay (Invitrogen) and a BioTek Synergy HT plate reader (BioTek Instruments Inc., Winooski, VT, USA), and quantified bacterial 16S gene copies using qPCR (primers: Supplementary Table 1) in triplicate. We used 8 µL reactions consisting of 0.2 mM of each primer, 0.01 U µL−1 Phusion HotStart II Polymerase (Thermo Fisher Scientific, Waltham, MA), 1× Phusion HF buffer (Thermo Fisher Scientific), 3.0 mM MgCl2, 6% glycerol, and 200 µL of dNTPs. We amplified DNA using a Bio-Rad CFX384 Touch real-time PCR detection system (Bio-Rad, Hercules, CA, USA) with the following cycling conditions: 95 °C at 1 min and 44 cycles of 95 °C (30 s), 64.5 °C (30 s), and 72 °C (1 min).We sequenced the 16S V4 region (primers: EMP standard 515F—806R; Supplementary Table 1) on an Illumina MiSeq (Illumina, Inc., San Diego, CA, USA). Sequences were amplified using the same reaction mix as qPCR amplification but cycling at 95 °C for 2 min followed by 15 cycles of 95 °C (30 s), 55 °C (30 s), and 60 °C (4 min). In addition to post-incubation soils, we extracted, amplified, and sequenced DNA of the bacterial community at the start of the incubation.Sequence processing and qSIP analysisThe raw sequence data of forward and reverse reads (FASTQ) were processed within the QIIME 2 environment (release 2018.6)29,30, denoising sequences with the available DADA2 pipeline31. We clustered the remaining sequences into amplicon sequence variants or ASVs (at 100% sequence identity) against the SILVA 132 database32 using an open-reference Naïve Bayes feature classifier33. We removed global singletons and doubleton ASVs, non-bacterial lineages, and samples with less than 4000 sequence reads. Removal of global singletons and doubletons resulted in the removal of 2241 unique ASVs from the feature table yielding 115,647 out of 117,888 (a retention of 98% of all ASVs) as well as the loss of 4018 sequences leaving 37,765,678 (a retention >99% of all sequences). We combined taxonomic information and ASV sequence counts with per-fraction qPCR and density measurements using the phyloseq package (version 1.24.2), in R (version 3.5.1)34. Because high-throughput sequencing produces relativized measures of abundance, we converted ASV sequencing abundances in each fraction to the number of 16S rRNA gene copies per g dry soil based on the known amount of dry soil added and the amount of DNA in each soil sample. All data and analytical code have been made publicly accessible35.To perform qSIP analysis and calculate per-capita growth rates of each ASV, we used our in-house qsip package (https://github.com/bramstone/qsip) based on previously published research7,10. Because rare and infrequent taxa are more likely to be lost in samples with poor sequencing depth with their absences affecting DNA density changes, we invoked a presence or absence-based filtering criteria on ASVs prior to calculation of per-capita growth rates. Within each ecosystem, we kept only ASVs that appeared in two of the three replicates of a treatment (18O, C, and C + N) and at that appeared in at least five of the fractions within each of those two replicates. ASVs filtered out of one treatment were allowed to appear in another if they met the frequency threshold.For all remaining ASVs (1081 representing less than 1% of all ASVs but 58% of all sequence reads), we calculated per-capita gross growth (i.e., cell division) rates observed in each replicate using an exponential growth model10. We applied these per-capita rates to the number of 16S rRNA gene copies to estimate the production of new 16S rRNA gene copies of each ASV per g dry soil per week using the following equation:$$frac{{rm{d}}{N}_{{rm{i}}}}{{{rm{d}}t}}={N}_{{rm{i,t}}}-{N}_{{rm{i,t}}}{e}^{-{g}_{{rm{i}}}t},$$
(1)
Where Ni,t is the number of 16S rRNA gene copies of taxon i at time t (here after 7 days) and gi represents the per-capita growth rate (calculated as a daily rate). See Supplementary Fig. 3 for results on the production of 16S gene copies.Calculation of 16S rRNA gene copy numbers and cell massIn parallel to taxonomic assignment, we compared quality-filtered 16S sequences against a database of 12,415 complete prokaryote genomes obtained from GenBank. From these genomes, we extracted data on 16S rRNA gene copy number, total genome size, and 16S gene sequence. We used BLAST to find matches against this database to the ASVs generated from QIIME 2 to make per-taxon assignments of 16S rRNA gene copy number and total genome size13. For ASVs that did not find an exact match, we assigned 16S rRNA gene copy number values and genome sizes based on the median values observed in the most specific possible taxonomic rank. We estimated the mass of individual cells for each population using published allometric scaling relationships between genome length and cellular mass from West and Brown:36$${{{log }}}_{10}({M}_{{rm{i}}})=frac{{{{log }}}_{10}left({G}_{{rm{i}}}right)-9.4}{0.24},$$
(2)
where Mi indicates cellular mass (g) and Gi indicates genome length (bp) for taxon i. We obtained this relationship by digitizing Fig. 436 using DataThief III and re-fitting the trend line in log–log space. We estimated that 20% of the cellular mass was carbon37. To validate this approach, cellular mass estimates and initial 16S copy number measurements were used to estimate population-level biomass C values which were summed and compared to initial community-level MBC. We found that these values overestimated initial MBC by an order of magnitude. As such, cellular carbon mass was divided by 10 in our final calculations. We applied cellular mass and 16S copy number estimates to the production of 16S copies to estimate the production of biomass carbon for each taxon during the incubation period (t):$${P}_{{rm{i}}}=frac{{rm{d}}{N}_{{rm{i}}}/{{rm{d}}t}}{C_{{rm{i}}}}cdot {M}_{{rm{i}}}cdot 0.2,$$
(3)
where Pi indicates production of biomass carbon (µg C g dry soil−1 week−1) and Ci indicates 16S copy number per cell for taxon i. The 0.2 coefficient represents an estimate that 20% of cellular mass is composed of carbon.Efficiency and respiration modelingWe estimated rates of respiration using qSIP-informed growth rates and community-level carbon use efficiency (CUE). CUE estimates were based on the incorporation of 18O-water into DNA as a measure of gross biomass production38,39 and measured CO2 in headspace gas from soil incubations. We calculated the production of 18O-labeled biomass carbon (18P) at the community-level for each sample by summing the products of per-taxon 18O enrichment (excess atom fraction, EAF) and relative abundance:$${, }^{18}{P}=mathop{sum }limits_{i=1}^{n}({,}^{18}{{{rm{EAF}}}}_{{rm{i}}}cdot {y}_{{rm{i}}})cdot {rm{DN}}{rm{A}}_{0}cdot fleft({{rm{MB}}}{rm{C}}_{0} sim {rm{DN}}{rm{A}}_{0}right),$$
(4)
where 18P indicates the gross production of 18O-labeled microbial biomass carbon per gram of dry soil per week, 18EAFi indicates the enrichment of DNA of taxon i and yi indicates its relative abundance, DNA0 indicates the concentration of DNA per gram of dry soil prior to incubation, and MBC0 indicates the microbial biomass carbon per gram of dry soil prior to incubation. Here, the MBC0 ~ DNA0 function indicates the linear relationship between MBC and DNA concentration. We used the output from Eq. 4 to calculate community CUE for each sample:$${{rm{CUE}}}=frac{{,}^{18}{{P}}}{(!{,}^{18}P+R)},$$
(5)
where R indicates the total CO2 respired per gram dry soil per week.We used the community CUE values from each sample (Eq. 5) to constrain/as upper and lower limits our estimates of per-taxon CUE. For a group of three replicates from a given ecosystem and treatment, we used the minimum and maximum observed community-level CUE values as the acceptable range of per-taxon CUE values. These constraints were used to control the shape of the function of per-taxon CUE and growth rate, though functions were modeled both with and without constraints (i.e., per-taxon CUE values were bounded only by 0 and 0.7). The range of community-level CUE values for each treatment were 0.18–0.53 for control soils, 0.04–0.13 for carbon amended soils and 0.03–0.08 for carbon and nitrogen amended soils and did not vary much between ecosystems. As a result of uncertainty in the literature about the relationship between growth rate and CUE14, several different relationships were postulated to model per-taxon CUE as a function of per-taxon growth rate: linear increase, linear decrease, exponential decrease, unimodal with peak CUE at growth rate of 0.5, and unimodal with peak CUE at a growth rate of 0.05 (the median of all per-taxon growth rates in the data). Comparisons between functions were made by calculating AIC values from per-taxon respiration, summed, and regressing against measured respiration values. Likewise, for each function, we tested how well per-taxon CUE estimates reconstructed community-level CUE by weighting the CUE value of each taxon by its relative abundance, summing, and regressing against community-level CUE. To select the best per-taxon CUE function, AIC values from both scaling efforts were combined. To make AIC values comparable, all respiration and CUE terms were z-transformed prior to regression scaling. To reflect our priority of estimating per-taxon respiration, AIC values from the respiration scaling regression models were multiplied by two and summed with AIC values from CUE scaling such that AICTotal = 2(AICResp) + AICCUE. Across these comparisons, the best estimate of per-taxon CUE was the unimodal function of growth rate, constrained by community-level CUE and peaking at growth rates of 0.5 (Table 1), such that:$${{rm{CUE}}}_{{rm{i}}}=-4({{rm{CUE}}}_{{rm{E}}{rm{:}}{rm{T}}{rm{:}}{{rm{range}}}})cdot {left({g}_{{rm{i}}}-0.5right)}^{2}+({{rm{CUE}}}_{{rm{E}}{rm{:}}{rm{T}}{rm{:}}{max }}),$$
(6)
where CUEi indicates per-taxon CUE, CUEE:T:max indicates the maximum CUE values observed for a group of replicates within a given ecosystem and treatment (E:T). With this function, higher per-capita growth rate values were parameterized to produce higher CUE values initially and then decrease reflecting a growth-CUE tradeoff14, here bound by the difference in maximum and minimum CUE values. We applied per-taxon CUE estimates from Eq. 6 to per-taxon growth rates to yield estimates of per-taxon respiration:$${r}_{{rm{i}}}={r}_{{rm{g,i}}}+{r}_{{rm{m,i}}}=left(frac{{g}_{{rm{i}}}}{{{rm{CUE}}}_{{rm{i}}}}-{g}_{{rm{i}}}right)+left(frac{{g}_{{rm{i}}}}{{{rm{CUE}}}_{{rm{i}}}}-{g}_{{rm{i}}}right)cdot beta,$$
(7)
where ri indicates per-capita respiration for taxon i, rg,i indicates growth-related respiration, rm,i indicates maintenance-related respiration, and β is a constant of 0.01 that represents the maintenance requirements as a proportion of total energy use40. We used these values of per-taxon, per-capita respiration rates to estimate per-taxon respiration per gram of dry soil per week:$${R}_{{rm{i}}}={P}_{{rm{i}}}cdot {r}_{{{rm{g,i}}}}+{P}_{{rm{i}}}cdot {r}_{{{rm{m,i}}}},$$
(8)
where Ri indicates respiration of CO2–C (µg C g dry soil−1 week−1) for taxon i.In addition to per-taxon respiration estimates based on 18O enrichment, we used another model for comparison. Here, respiration was calculated based on 16S abundance alone:$${R}_{{rm{i}}}={N}_{{rm{i}}}cdot f(R sim N+0),$$
(9)
where Ni indicates final 16S abundance for taxon i, R indicates microbial respiration of CO2-C (µg C g dry soil−1 week−1) and N indicates total 16S abundance at the end of the incubation. Here, the R ~ N function indicates the linear relationship, with an intercept of 0, between CO2 respiration and 16S gene concentration across all samples.Diversity, compositional, and statistical analysisFor patterns of evenness in bacterial carbon use and relative abundance, we used Pielou’s evenness which is the quotient of Shannon’s diversity and the observed richness. For each sample, we applied Pielou’s evenness to bacterial abundances as well as bacterial carbon use (relativized to sum to one, in both cases).We created a linear mixed model to test the relationship between the carbon use (the sum of biomass production and respiration) and relative abundance of bacterial genera from the dominant phyla, which accounted for >90% of all C flux. Here, we averaged carbon use and relative abundance for all replicates in a given ecosystem and treatment. We used the lme4 R package (version 1.1-20)41 and obtained p-values using the Satterthwaite method in the lmerTest R package (version 3.1-0)42. To limit pseudo-replication, we accounted for differences in carbon use across ecosystems and due to bacterial Genus by implementing random intercepts. We selected for the optimal random and fixed components by dropping individual terms and comparing models with likelihood ratio tests, disregarding models that failed to converge. Our final model fit was:$${{{log }}}_{10}({C}_{{rm{i}}}) sim {{{log }}}_{10}left({y}_{{rm{i}}}right)ast T+left(1|Eright)+(1|{{rm{Genus}}}),$$
(10)
where Ci indicates the relativized carbon use for taxon i (averaged across all three replicates in a given ecosystem and treatment), yi indicates the relative abundance of taxon i (averaged across all three replicates), T indicates soil treatment, and E indicates ecosystem.For differences in composition, we created species abundance tables using the traditional abundances, as well as measures of carbon use (growth and maintenance respiration) of each ASV in each sample. To account for differences in absolute abundances and flux rates between sites, we relativized all abundance tables. We summarized compositional differences using Bray–Curtis dissimilarities then identified multivariate centroids for all replicates in a site by treatment group. We tested the effect of site and nutrient amendment on the resulting group centroids using PERMANOVA tests implemented with the adonis function in the vegan package (version 2.5-3)43. We related compositional shifts in relative abundance to those in relativized growth and maintenance using Mantel tests with the mantel function in vegan.To test for changes in the type of soil C preferred by microbial genera (either 13C-labeled glucose or 12C soil carbon) in response to nitrogen addition, we used Levene’s test with the car package (version 3.0-10)44. Specifically, we analyzed the relationship between 13C use and 12C use (both relativized) on bacterial genera across all replicates and in C and C + N treatments using a linear model. We then extracted model residuals and tested whether variance was significantly different across treatments by focusing on the interaction between individual replicates and treatment. This produced a significance test describing treatment-level differences in 13C–12C use.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Global scientific partnerships should generate and share knowledge equitably, but too often exploit research partners in lower-income countries, while disproportionately benefitting those in higher-income countries. Here, I outline my suggestions for more-equitable partnerships.International scientific collaboration is meant to bring together knowledge and resources to solve humanity’s most pressing problems. Scientists pursue collaborations for many different reasons, from learning, testing and integrating approaches, to sharing and developing new ideas. Collaborations can also help institutions in low- and medium-income countries to access resources they lack, build capacity and increase scientific visibility including through publications and citations. While language1 and other cultural barriers prevent an even geographical distribution of authorships, readership and editorial processes2,3, structural power imbalances in international collaborations remain largely unexplored. My goal, as a Colombia-based scientist with 23 years of experience of international collaboration, is to reflect on how these imbalances are too often embedded in scientific practices between scientists from high- and lower-income nations. These imbalances can result in extractive partnerships where benefits flow in only one direction and may even impoverish research in the disadvantaged country by removing experience and not contributing to local capacity and infrastructure. This practice has been termed ‘helicopter’, ‘parachute’ or ‘colonial’ science4. After years of observing and experiencing the effects, both positive and negative, of the ways in which science and research collaborations in the developing world unfold, and given the prevalence of many unhealthy practices, I propose some guidelines to make international collaboration more inclusive, equitable and in the end more meaningful and relevant for all. More

A study on the global burden of disease conducted by the Institute for Health Metrics and Evaluation (IHME) showed that air pollution is the fifth highest risk factor for mortality worldwide and the leading environmental risk factor; air pollution is responsible for 4.2 million deaths annually1,2. Among various air pollutants, fine particulate matter measuring 2.5 µm or less in aerodynamic diameter (PM2.5) is sufficiently small to penetrate the lungs deeply and pass into the blood stream. This may cause cardiovascular and respiratory diseases, such as lower respiratory infection (LRI), ischemic heart disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), and lung cancer1,2,3.During the period 2000–2015, when the annual GDP growth rate in India exceeded 8%4, the number of premature deaths attributable to PM2.5 exposure increased from 857,300 to 1,090,400 people1. In 2015, PM2.5-related premature deaths in India accounted for a quarter of global deaths attributed to PM2.5, a level that was comparable to that of China, which has some of the world’s highest air pollution levels1.India’s rapid economic growth between 1995 and 2009 was mainly due to increasing fixed capital formation (i.e., final demand), and the additional capital formation (i.e., investment) was attributed to a marked increase in coal consumption in India during the same period; coal consumption is one of the major sources of PM2.5 emissions5. Thus, to reduce premature deaths related to PM2.5 emissions in India, it is considered important for Indian policymakers to develop effective demand- and supply-side policy with a focus on higher priority sectors.In 2019, the Indian government launched the National Clean Air Programme (NCAP) to achieve its sustainable development goals; the proposed national target was a 20–30% reduction in PM2.5 and PM10 levels by 20246. This is the first time-bound commitment concerning air pollution that has been promulgated in India. Although the NCAP mentioned the importance of adopting a multi-sectoral and collaborative approach6, concrete collaborative policies have not yet been developed. To develop effective demand- and supply-side policies, it is important to obtain a deeper understanding of the supply chain structure centered around a critical sector that has contributed to PM2.5 emissions—and therefore, premature deaths—in India.According to the Regional Emission Inventory in Asia (REAS) database for emissions from 2000 to 20087, the power generation sector is one of the largest contributors of PM2.5 emissions in India, accounting for 822,000 tons of PM2.5 in 2008. In addition, the emissions from the power generation sector increased consistently from 2000 to 2008. Considering energy sources for electrical power generation in India, coal-fired thermal power accounted for 68% of the total 462 TWh generated in 20078. However, coal-fired thermal power plants were responsible for more than 90% of PM2.5 emissions in the power generation sector in 20077, which means that coal-fired thermal power is the most emission-intensive sector and that it plays a critical role in the emissions-related health impact on the people of India. This study examined power generation sector including the coal-fired thermal power and oil-fired thermal power generation, biomass power generation, which account for the remaining 10% of PM2.5 emissions as a critical emission source sector.PM2.5 emissions from the electric power sector have been increasing due to the increases in electric power consumption that is directly necessary for households, and for industries that produce “final” goods and services. In addition to direct electric power use, it is also important to note that both consumers, i.e., households and industry, also indirectly consume electric power through the production of “intermediate” goods and services (including electric power) that are required to produce the final goods and services. It is also important to note that both direct and indirect electric power consumption generate PM2.5 emissions.The electric power generation sector plays an important role in the supply chain9. To effectively mitigate the health impacts related to PM2.5 emissions in India, the PM2.5 emissions associated with the indirect use of electricity (i.e., Scope 3 emissions from the electricity sector in line with the greenhouse gas [GHG] protocol10, as well as emissions associated with the direct use of electricity (i.e., Scope 2 emissions from the electricity sector in line with the GHG protocol11) need to be reduced. In other words, it is necessary to identify environmentally important supply chain paths that have the greatest mitigation potential for health impacts in India.A highly relevant study by Guttikunda and Jawahar (2014)12 focused on coal-fired power plants located in Indian states in 2010 and estimated the total annual PM2.5 emissions in India at around 580,000 tons. These authors also estimated that the annual PM2.5-induced mortalities in India were between 80,000 and 115,000. However, because the study of Guttikunda and Jawahar (2014)12 only examined “production-based” PM2.5 emissions and production-based mortality risks, these results provide a relatively limited understanding of how the final demand of countries such India affects PM2.5-induced mortality risks.Nansai et al. (2020)13 quantified the mortality-based economic losses (i.e., income loss) attributed to primary and secondary PM2.5 emissions in individual Asian countries that were induced by the final demand of the world’s five largest consuming countries. Their findings showed that in 2010, consumption in the USA, China, Japan, Germany, and the United Kingdom caused approximately 2000, 7700, 2700, 3300, and 3400 deaths in India, respectively. These deaths resulted in economic losses in India of 0.14, 0.26, 0.087, 0.11, and 0.11 billion US dollars in purchasing power parity, respectively. In India, particularly, the export of goods and services from India to these developed countries contributed considerably to PM2.5 emissions, and therefore the high number of premature deaths in India. This situation calls for an analysis of how the global supply chain is impacting health in India in terms of emission responsibility14. In addition, domestic policies need to be introduced to mitigate air pollution inside India, and demand-side policies that consider the role of consumers outside India need to be developed.Structural path analysis (SPA) is a well-known and effective method that was first introduced by Defourny and Thorbecke (1984)15 to trace important supply chain paths from complex input–output structures by decomposing matrix products into elements (paths). Previous studies addressing PM2.5 emissions have applied this method. For example, Meng et al. (2015)16 identified PM2.5 emission-intensive supply chain paths in China using SPA. However, they only considered PM2.5 emissions and did not consider the reduction potential of health impacts. Nagashima et al. (2017)17 identified critical supply chain paths that contribute toward premature deaths in East Asian countries; however, they did not include secondary PM2.5 generation, which has a marked influence on health, and they did not consider India in their analysis.This study used EXIOBASE 3 data for 2010 and applied an SPA18,19,20,21 to identify important supply chain paths driven by domestic and international demands that contribute to primary and secondary PM2.5 emissions from the power sector, which is an environmentally critical sector in India. We introduced an atmospheric transport model to fully link final demand via supply chains to the primary emitter that is the power sector in India. Finally, we linked the atmospheric transport of emissions from the emitter to the impact on health in India. To the best of our knowledge, this study is the first attempt to estimate consumption-based PM2.5 emissions as well as the consumption-based mortality risk in India by using a combined approach that is based on an environmentally extended multi-regional input–output (MRIO) analysis and an atmospheric transport model.The remainder of this manuscript is structured as follows: “Methodology” section explains our methodology, “Data and computation” section describes the data, “Results” section presents and discusses the results, and finally, “Discussion and conclusion” section contains the discussion and conclusions. More

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