Systematizing the approach to air quality measurement and analysis in low and middle income countries

Peer reviewe

Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India

Since at least the 1980s, many farmers in northwest India have switched to mechanized combine harvesting to boost efficiency. This harvesting technique leaves abundant crop residue on the fields, which farmers typically burn to prepare their fields for subsequent planting. A key question is to what extent the large quantity of smoke emitted by these fires contributes to the already severe pollution in Delhi and across other parts of the heavily populated Indo-Gangetic Plain located downwind of the fires. Using a combination of observed and modeled variables, including surface measurements of PM2.5, we quantify the magnitude of the influence of agricultural fire emissions on surface air pollution in Delhi. With surface measurements, we first derive the signal of regional PM2.5 enhancements (i.e. the pollution above an anthropogenic baseline) during each post-monsoon burning season for 2012–2016. We next use the Stochastic Time-Inverted Lagrangian Transport model (STILT) to simulate surface PM2.5 using five fire emission inventories. We reproduce up to 25% of the weekly variability in total observed PM2.5 using STILT. Depending on year and emission inventory, our method attributes 7.0%–78% of the maximum observed PM2.5 enhancements in Delhi to fires. The large range in these attribution estimates points to the uncertainties in fire emission parameterizations, especially in regions where thick smoke may interfere with hotspots of fire radiative power. Although our model can generally reproduce the largest PM2.5 enhancements in Delhi air quality for 1–3 consecutive days each fire season, it fails to capture many smaller daily enhancements, which we attribute to the challenge of detecting small fires in the satellite retrieval. By quantifying the influence of upwind agricultural fire emissions on Delhi air pollution, our work underscores the potential health benefits of changes in farming practices to reduce fires

The growing contribution of sulfur emissions from ships in Asian waters, 1988–1995

International shipping is a major source of sulfur emissions in Asia. Because the fuel oil used by ships is high in sulfur, the resulting emissions of SO2 are large and contribute as much as 20% to the atmospheric loading in the vicinity of ports and heavily traveled waterways. Because of the rapid growth of Asian economies in the 1980s and early 1990s, it is estimated that shipping trade grew by an average of 5.4% per year between 1988 and 1995; in particular, crude oil shipments to Asian countries other than Japan grew by an average of 11.4% per year. The emissions of SO2 from shipping are estimated to have grown by 5.9% per year between 1988 and 1995, rising from 545 Gg in 1988 to 817 Gg in 1995. This study uses the ATMOS atmospheric transport and deposition model to study the effects of these emissions, both in absolute terms and relative to land-based emissions, on wet and dry deposition of sulfur. Southeast Asia is most heavily affected by emissions from ships, particularly Sumatra, peninsular Malaysia, and Singapore, which routinely receive in excess of 10% of their deposition from ships. A strong seasonal component is also observed, with large areas of Southeast Asia and coastal Japan receiving sulfur deposition that exceeds 10 mg S m−2 season−1. Deposition is at least 25% higher in summer and fall than in winter and spring. Peak values of 25–50 mg S m−2 season−1 are calculated for winter in the Strait of Malacca. This work suggests a need to introduce policies to reduce the sulfur content of marine fuels or otherwise reduce emissions of SO2 from ships in Asian waters

Estimates of Health Impacts and Radiative Forcing in Winter Haze in Eastern China through Constraints of Surface PM_2.5 Predictions

The Gridpoint Statistical Interpolation (GSI) Three-Dimensional Variational (3DVAR) data assimilation system is extended to treat the MOSAIC aerosol model in WRF-Chem, and to be capable of assimilating surface PM_2.5 concentrations. The coupled GSI-WRF-Chem system is applied to reproduce aerosol levels over China during an extremely polluted winter month, January 2013. After assimilating surface PM_2.5 concentrations, the correlation coefficients between observations and model results averaged over the assimilated sites are improved from 0.67 to 0.94. At nonassimilated sites, improvements (higher correlation coefficients and lower mean bias errors (MBE) and root-mean-square errors (RMSE)) are also found in PM_2.5, PM₁₀, and AOD predictions. Using the constrained aerosol fields, we estimate that the PM_2.5 concentrations in January 2013 might have caused 7550 premature deaths in Jing-Jin-Ji areas, which are 2% higher than the estimates using unconstrained aerosol fields. We also estimate that the daytime monthly mean anthropogenic aerosol radiative forcing (ARF) to be −29.9W/m² at the surface, 27.0W/m² inside the atmosphere, and −2.9W/m² at the top of the atmosphere. Our estimates update the previously reported overestimations along Yangtze River region and underestimations in North China. This GSI-WRF-Chem system would also be potentially useful for air quality forecasting in China