A novel approach to deriving the fine-scale daily NO2 dataset during 2005-2020 in China: Improving spatial resolution and temporal coverage to advance exposure assessment.

Surface NO2 pollution can result in serious health consequences such as cardiovascular disease, asthma, and premature mortality. Due to the extensive spatial variation in surface NO2, the spatial resolution of a NO2 dataset has a significant impact on the exposure and health impact assessment. There is currently no long-term, high-resolution, and publicly available NO2 dataset for China. To fill this gap, this study generated a NO2 dataset named RBE-DS-NO2 for China during 2005-2020 at 1 km and daily resolution. We employed the robust back-extrapolation via a data augmentation approach (RBE-DA) to ensure the predictive accuracy in back-extrapolation before 2013, and utilized an improved spatial downscaling technique (DS) to refine the spatial resolution from 10 km to 1 km. Back-extrapolation validation based on 2005-2012 observations from sites in Taiwan province yielded an R2 of 0.72 and RMSE of 10.7 µg/m3, while cross-validation across China during 2013-2020 showed an R2 of 0.73 and RMSE of 9.6 µg/m3. RBE-DS-NO2 better captured spatiotemporal variation of surface NO2 in China compared to the existing publicly available datasets. Exposure assessment using RBE-DS-NO2 show that the population living in non-attainment areas (NO2 ≥ 30 µg/m3) grew from 376 million in 2005 to 612 million in 2012, then declined to 404 million by 2020. Unlike this national trend, exposure levels in several major cities (e.g., Shanghai and Chengdu) continued to increase during 2012-2020, driven by population growth and urban migration. Furthermore, this study revealed that low-resolution dataset (i.e., the 10 km intermediate dataset before the downscaling) overestimated NO2 levels, due to the limited specificity of the low-resolution model in simulating the relationship between NO2 and the predictor variables. Such limited specificity likely biased previous long-term NO2 exposure and health impact studies employing low-resolution datasets. The RBE-DS-NO2 dataset enables robust long-term assessments of NO2 exposure and health impacts in China.

Assessment of agricultural pesticide inert ingredient transport following modeling approach: Case study of two formulation agents in Sacramento River watershed.

This research provides the first assessment of the environmental fate and transport of agricultural pesticide formulation agents following a dynamic modeling approach. Two formulation agents of toxicological concern, Naphthalene and Solvent Naphtha (Petroleum), Heavy Aromatic, were simulated from their usage in commercially-applied pesticides. The Soil and Water Assessment Tool (SWAT) was applied to simulate these formulation agents during 2011-2014 in the agriculturally intensive Sacramento River watershed. The sensitivity and uncertainty of some key parameters were analyzed. The predicted transport masses of these formulation agents in surface water were strongly associated with rainfall. While predicted transport masses were quite small at the watershed scale (<0.01% of applied masses), they were 26-31 times higher in certain locales at the subbasin level. Since many formulation agents are widely used in pesticides throughout this and other agriculturally impacted watersheds, their potential risks in the environment need more thorough investigation by modeling and monitoring, especially for areas with heavy usage.

Assessment of pyrethroid contamination and potential mitigation strategies in California Central Coast surface waters.

Increased use of pyrethroids in the Central Coast of California since 2011 has resulted in a dramatic increase in the number and proportion of surface water samples with detectable concentrations at levels of concern to the public and state regulators. The goals of this study were to investigate the relationships between pyrethroid usage and environmental contamination, quantify and assess the potential risks, and recommend mitigation strategies. This study compiled the available pyrethroid use and surface water sampling data for the region, and then applied GIS methods to dynamic simulation modeling and usage-restriction buffer analyses. The results showed that in Monterey County alone, the agricultural usages of bifenthrin and permethrin each increased by ~50%, and the positive detection frequencies of both also increased around 2011-2013. County-wide, bifenthrin positive detections in surface water samples increased precipitously from 8.2% (7/85) for 2008-2012 up to 36.4% (106/291) for 2013-2017, and detections above its crustacean LC50 concentration went from 7.1% (6/85) to 35.7% (104/291). Despite its higher usage by mass, comparable figures for permethrin were more modest for the same time-periods, with positive detections going from 10.6% (9/85) to 14.4% (64/444), and detections above its crustacean LC50 going from 3.5% (3/85) to 7.2% (32/444). The seasonal lag between high bifenthrin usage in spring/summer and high detections in fall/winter samples showed the best correlations with 128- to 182-day lag times. This timing suggests that fallow season rain is likely the main driver of pyrethroid off-site movement into surface waters. SWAT modeling indicated that significant reductions in surface water permethrin concentrations only occurred with buffer distances of 1.6-3.2 km, but not with narrower buffers. However, if those wider buffers were implemented, permethrin could no longer be used on the majority of land where it is currently applied. Specifically, a 1.6-km buffer reduced the instream concentration by 8% but impacted 50% of the cropland, and a 3.2-km buffer reduced the concentration by 50% while impacting 76% of cropland. This study suggested that more promising alternative management practices could include an overall reduction in pyrethroid usage back to 2011 levels or other active mitigation strategies, like planting cover crops during the fallow winter wet season, or installing either vegetated buffer strips and/or sediment check dams on small tributaries to minimize sediment runoff.

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.

In China, ozone pollution shows an increasing trend and becomes the primary air pollutant in warm seasons. Leveraging the air quality monitoring network, a random forest model is developed to predict the daily maximum 8-h average ozone concentrations ([O3]MDA8) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O3]MDA8 by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R2 = 0.69 and RMSE = 26 µg/m3). Compared with chemical transport models that require a plenty of variables and expensive computation, the random forest model shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost. The nationwide population-weighted [O3]MDA8 is predicted to be 84 ± 23 µg/m3 annually, with the highest seasonal mean in the summer (103 ± 8 µg/m3). The summer [O3]MDA8 is predicted to be the highest in North China (125 ± 17 µg/m3). Approximately 58% of the population lives in areas with more than 100 nonattainment days ([O3]MDA8>100 µg/m3), and 12% of the population are exposed to [O3]MDA8>160 µg/m3 (WHO Interim Target 1) for more than 30 days. As the most populous zones in China, the Beijing-Tianjin Metro, Yangtze River Delta, Pearl River Delta, and Sichuan Basin are predicted to be at 154, 141, 124, and 98 nonattainment days, respectively. Effective controls of O3 pollution are urgently needed for the highly-populated zones, especially the Beijing-Tianjin Metro with seasonal [O3]MDA8 of 140 ± 29 µg/m3 in summer. To the best of the authors' knowledge, this study is the first statistical modeling work of ambient O3 for China at the national level. This timely and extensively validated [O3]MDA8 dataset is valuable for refining epidemiological analyses on O3 pollution in China.