Effects of historical food production, consumption and trade on agricultural ammonia emissions and fine particulate matter (PM2.5) pollution worldwide: Implications for food-system mitigation strategies for a sustainable future
Bibliographic record
Abstract
Fine particulate matter (PM2.5) pollution threatens human lives and wellbeing worldwide. Agricultural ammonia (NH3) is a key precursor of PM2.5. To examine how food consumption, production, and trade in different countries and regions affect global air quality, we derived a half-century (1962–2018) crop- and livestock-specific agricultural NH3 emission inventory and used it to conduct numerical experiments with the GEOS-Chem chemical transport model to estimate the impacts of food production, consumption, and trade in nine major food-importing and food-exporting countries or regions (China, India, Japan, Russia, Argentina, Brazil, Canada, European Union, USA) on PM2.5 pollution in themselves and in other countries via both atmospheric transport and food trade. We further performed sensitivity experiments by deducting NH3 emissions related to different food items that are consumed domestically vs. exported for each major country or region. We found that the rise in domestic food and feed crop consumption contribute significantly to PM2.5 pollution in China and India (up to ~40% of the total PM2.5 increase from all sources), among which ~40% is driven by meat production and consumption, highlighting the environmental impacts of dietary changes. We also found that even though China and India consume substantial amount of food imported from other countries, it is not a major contributor to PM2.5pollution in the exporting countries (e.g., ~1% of total PM2.5 in the food trading partners), mostly because the majority of domestic food demand is still satisfied by domestic production, and food import is diversified among a basket of exporting countries. Furthermore, agricultural NH3 is found to have a crucial modulating influence on PM2.5; e.g., the increase in PM2.5 due to agricultural NH3 could partly offset the decrease in PM2.5 induced by other anthropogenic emissions in North America after 1990, and such a phenomenon is expected for China as significant controls of non-agricultural emissions are underway. Our study highlights the significance of food consumption, production and trade in shaping PM2.5 worldwide, and it is important to incorporate sustainable food-system and agricultural strategies to simultaneously safeguard food security as well as the health of citizens and our planet.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".