Integrated assessment of global climate, air pollution, and dietary, malnutrition and obesity health impacts of food production and consumption between 2014 and 2018

Abstract

Agriculture accounts for approximately 10% of global greenhouse gas emissions and is simultaneously associated with impacts on human health through food consumption, and agricultural air pollutant emissions. These impacts are often quantified separately, and there is a lack of modelling tools to facilitate integrated assessments. This work presents a new model that integrates assessment of agricultural systems on (i) human health indirectly through dietary, obesity and malnutrition health risks from food consumption, (ii) human health directly through exposure to air pollutants from agricultural emissions, and (iii) greenhouse gas emissions. In the model, national food demand is the starting point from which the livestock and crop production systems that meet this are represented. The model is applied for 2014–2018 to assess the robustness of the GHG emissions and health burden results that this integrated modelling framework produces compared to previous studies that have quantified these variables independently. Methane and nitrous oxide emissions globally in 2018 were estimated to be 129 and 4.4 million tonnes, respectively, consistent with previous estimates. Agricultural systems were also estimated to emit 44 million tonnes of ammonia. An estimated 4.1 million deaths were associated with dietary health risks, 6.0 million with overweight/obesity, and 730 thousand infant deaths from malnutrition, consistent with previous studies. Agricultural air pollutant emissions were estimated to be associated with 537 thousand premature deaths attributable to fine particulate matter (PM2.5) exposure, and 184 thousand premature deaths from methane-induced ground-level ozone. These health impacts provide substantial opportunities to design integrated strategies that mitigate climate change, and improve human health, and also highlight possible trade-offs that the expansion of agricultural production could have due to increased emissions. The model presented here provides for the consistent evaluation of the implications of different agricultural strategies to meet food demand while minimising human health and climate change impacts.

Green food supply chain management as a solution for the mitigation of food supply chain management risk for improving the environmental health level

“…Although food is necessary for good health and well-being; emissions from food production have indirect effects on human health due to the exposure to harmful food industry toxins generation [ 37 ]. This is why food safety and security are critical issues around the world.…”

Integrated climate change and air pollution mitigation assessment for Togo

“…to be included in the modelling of GHG emissions. Therefore, a key improvement to Togo's agricultural GHG mitigation assessment would be the collection of data and development of an IPCC Tier 2 method model for agricultural GHG emissions (FAO, 2018;Malley et al, 2021a).…”

Sources of PM2.5‐Associated Health Risks in Europe and Corresponding Emission‐Induced Changes During 2005–2015

“…( 2015 ) and Malley et al. ( 2021 ). The coarse spatial distributions contribute to significant uncertainties in the health assessments, especially in polluted or populated areas (Li et al., 2016 ; Punger & West, 2013 ).…”

“…Adjoint models provide an alternative approach to efficiently calculate the response of a particular receptor function (e.g., PM 2.5 concentration, PM 2.5 ‐related premature deaths) to a large number of sources, with which detailed contributions from all kinds of emissions can be mapped out in health assessment studies (Lee et al., 2015 ; Malley et al., 2021 ; Nawaz & Henze, 2020 ; Nawaz et al., 2021 ; Pappin & Hakami, 2013 ). Compared to traditional model calculations of changes in the final state (e.g., concentrations) induced by a perturbation in model parameters (e.g., emissions), the adjoint method is a receptor‐oriented approach which calculates the sensitivities of the final state to a series of model parameters by transforming the changes in the final state backward in time (Henze et al., 2007 , 2009 ).…”