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      Air quality–related health damages of food

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          Poor air quality is the largest environmental health risk in the United States and worldwide, and agriculture is a major source of air pollution. Nevertheless, air quality has been largely absent from discussions about the health and environmental impacts of food. We estimate the air quality–related health impacts of agriculture in the United States, finding that 80% of the 15,900 annual deaths that result from food-related fine particulate matter (PM 2.5) pollution are attributable to animal-based foods. By estimating these impacts and exploring how to reduce them, this work fills a critical knowledge gap. Our results are relevant to food producers, processors, and distributors, and to policymakers and members of the public interested in minimizing the negative consequences of food.

          Abstract

          Agriculture is a major contributor to air pollution, the largest environmental risk factor for mortality in the United States and worldwide. It is largely unknown, however, how individual foods or entire diets affect human health via poor air quality. We show how food production negatively impacts human health by increasing atmospheric fine particulate matter (PM 2.5), and we identify ways to reduce these negative impacts of agriculture. We quantify the air quality–related health damages attributable to 95 agricultural commodities and 67 final food products, which encompass >99% of agricultural production in the United States. Agricultural production in the United States results in 17,900 annual air quality–related deaths, 15,900 of which are from food production. Of those, 80% are attributable to animal-based foods, both directly from animal production and indirectly from growing animal feed. On-farm interventions can reduce PM 2.5-related mortality by 50%, including improved livestock waste management and fertilizer application practices that reduce emissions of ammonia, a secondary PM 2.5 precursor, and improved crop and animal production practices that reduce primary PM 2.5 emissions from tillage, field burning, livestock dust, and machinery. Dietary shifts toward more plant-based foods that maintain protein intake and other nutritional needs could reduce agricultural air quality–related mortality by 68 to 83%. In sum, improved livestock and fertilization practices, and dietary shifts could greatly decrease the health impacts of agriculture caused by its contribution to reduced air quality.

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          Most cited references35

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          Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems

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            Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

            Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. Funding Bill & Melinda Gates Foundation.
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              Reducing food’s environmental impacts through producers and consumers

              Food's environmental impacts are created by millions of diverse producers. To identify solutions that are effective under this heterogeneity, we consolidated data covering five environmental indicators; 38,700 farms; and 1600 processors, packaging types, and retailers. Impact can vary 50-fold among producers of the same product, creating substantial mitigation opportunities. However, mitigation is complicated by trade-offs, multiple ways for producers to achieve low impacts, and interactions throughout the supply chain. Producers have limits on how far they can reduce impacts. Most strikingly, impacts of the lowest-impact animal products typically exceed those of vegetable substitutes, providing new evidence for the importance of dietary change. Cumulatively, our findings support an approach where producers monitor their own impacts, flexibly meet environmental targets by choosing from multiple practices, and communicate their impacts to consumers.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc Natl Acad Sci U S A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                18 May 2021
                10 May 2021
                10 May 2021
                : 118
                : 20
                : e2013637118
                Affiliations
                [1] aDepartment of Bioproducts and Biosystems Engineering, University of Minnesota , St. Paul, MN 55108;
                [2] bOxford Martin School, Nuffield Department of Population Health, University of Oxford , Oxford OX3 7DQ, United Kingdom;
                [3] cDepartment of Civil and Environmental Engineering, Carnegie Mellon University , Pittsburgh, PA 15213;
                [4] dDepartment of Civil & Environmental Engineering, University of Washington , Seattle, WA 98195;
                [5] eDepartment of Engineering and Public Policy, Tepper School of Business, Carnegie Mellon University , Pittsburgh, PA 15213;
                [6] fDepartment of Chemical Engineering, Carnegie Mellon University , Pittsburgh, PA 15213;
                [7] gDepartment of Applied Economics, University of Minnesota , St. Paul, MN 55108;
                [8] hDepartment of Mechanical Engineering, Carnegie Mellon University , Pittsburgh, PA 15213;
                [9] iDepartment of Civil and Environmental Engineering, University of Illinois , Urbana, IL 61801;
                [10] jDepartment of Ecology, Evolution, and Behavior, University of Minnesota , St. Paul, MN 55108
                Author notes
                2To whom correspondence may be addressed. Email: hill0408@ 123456umn.edu .

                Edited by Paul Behrens, Leiden University, Leiden, The Netherlands, and accepted by Editorial Board Member B. L. Turner March 18, 2021 (received for review June 30, 2020)

                Author contributions: N.G.G.D., S.B., S.K.T., M.A.C., and J.D.H. designed research; N.G.G.D., S.B., S.K.T., M.A.C., and J.D.H. performed research; P.J.A., J.D.M., N.Z.M., C.W.T., and P.T. contributed new reagents/analytic tools; N.G.G.D., S.B., S.K.T., M.A.C., and J.D.H. analyzed data; and N.G.G.D., S.B., S.K.T., M.A.C., P.J.A., J.D.M., N.Z.M., S.N.P., S.P., A.L.R., C.W.T., D.T., P.T., and J.D.H. wrote the paper.

                1S.B. and S.K.T. contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-9104-0192
                https://orcid.org/0000-0003-2205-3333
                https://orcid.org/0000-0001-7161-7751
                https://orcid.org/0000-0003-0041-058X
                https://orcid.org/0000-0003-4087-1209
                https://orcid.org/0000-0003-1712-6526
                https://orcid.org/0000-0003-4934-2434
                https://orcid.org/0000-0002-1819-083X
                https://orcid.org/0000-0001-6395-7676
                https://orcid.org/0000-0003-4763-1101
                https://orcid.org/0000-0001-7609-6713
                Article
                202013637
                10.1073/pnas.2013637118
                8158015
                33972419
                a1c8de86-56af-4d04-ba32-e2ff0addf6e8
                Copyright © 2021 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 6
                Funding
                Funded by: U.S. Environmental Protection Agency (EPA) 100000139
                Award ID: R835873
                Award Recipient : Peter Adams Award Recipient : Julian D. Marshall Award Recipient : Nicholas Z. Muller Award Recipient : Spyros N. Pandis Award Recipient : Stephen Polasky Award Recipient : Allen L. Robinson Award Recipient : Jason Hill
                Funded by: USDA | National Institute of Food and Agriculture (NIFA) 100005825
                Award ID: MIN-12-083
                Award Recipient : Jason Hill
                Funded by: USDA | National Institute of Food and Agriculture (NIFA) 100005825
                Award ID: MIN-12-110
                Award Recipient : Jason Hill
                Categories
                9
                434
                Physical Sciences
                Sustainability Science
                Social Sciences
                Sustainability Science

                air quality,agriculture,fine particulate matter,food,pollution

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