Ammonia (NH3) emissions from fertilizer application is a highly uncertain input to chemical transport models (CTMs). Reducing such uncertainty is important for improving predictions of ambient NH3and PM2.5concentrations, for regulatory and policy purposes and for exploring linkages of air pollution to human health and ecosystem services. Here, we implement a spatially and temporally resolved inventory of NH3emissions from fertilizers, based on high-resolution crop maps, crop nitrogen demand and a process model, as input to the Comprehensive Air Quality Model with Extensions (CAMx). We also examine sensitivity to grid resolution, by developing inputs at 12 km × 12 km and 4 km × 4 km, for the Corn Belt region in the Midwest United States, where NH3emissions from chemical fertilizer application contributes to approximately 50% of anthropogenic emissions. Resulting predictions of ambient NH3and PM2.5concentrations were compared to predictions developed using the baseline 2011 National Emissions Inventory, and evaluated for closure with ground observations for May 2011. While CAMx consistently underpredicted NH3concentrations for all scenarios, the new emissions inventory reduced bias in ambient NH3concentration by 33% at 4 km × 4 km, and modestly improved predictions of PM2.5, at 12 km × 12 km (correlation coefficients r = 0.57 for PM2.5, 0.88 for PM-NH4, 0.71 for PM-SO4, 0.52 for PM-NO3). Ourmore »
Scaling up electric vehicles (EVs) provides an avenue to mitigate both carbon emissions and air pollution from road transport. The benefits of EV adoption for climate, air quality, and health have been widely documented. Yet, evidence on the distribution of these impacts has not been systematically reviewed, despite its central importance to ensure a just and equitable transition. Here, we perform a systematic review of recent EV studies that have examined the spatial distribution of the emissions, air pollution, and health impacts, as an important aspect of the equity implications. Using the Context-Interventions-Mechanisms-Outcome framework with a two-step search strategy, we narrowed down to 47 papers that met our inclusion criteria for detailed review and synthesis. We identified two key factors that have been found to influence spatial distributions. First, the cross-sectoral linkages may result in unintended impacts elsewhere. For instance, the generation of electricity to charge EVs, and the production of batteries and other materials to manufacture EVs could increase the emissions and pollution in locations other than where EVs are adopted. Second, since air pollution and health are local issues, additional location-specific factors may play a role in determining the spatial distribution, such as the wind transport of more »
- Publication Date:
- NSF-PAR ID:
- 10408759
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 5
- Page Range or eLocation-ID:
- Article No. 053001
- ISSN:
- 1748-9326
- Publisher:
- IOP Publishing
- Sponsoring Org:
- National Science Foundation
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Clinical trial registry ClinicalTrials.gov (National Institutes of Health); Trial Registration Number:NCT04633135 ; Date of Registration: 11 November 2020 – Retrospectively registered.URL:
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