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Abstract Studies of wintertime air quality in the North China Plain (NCP) show that particulate‐nitrate pollution persists despite rapid reduction in NOxemissions. This intriguing NOx‐nitrate relationship may originate from non‐linear nitrate‐formation chemistry, but it is unclear which feedback mechanisms dominate in NCP. In this study, we re‐interpret the wintertime observations of17O excess of nitrate (∆17O(NO3−)) in Beijing using the GEOS‐Chem (GC) chemical transport model to estimate the importance of various nitrate‐production pathways and how their contributions change with the intensity of haze events. We also analyze the relationships between other metrics of NOychemistry and [PM2.5] in observations and model simulations. We find that the model on average has a negative bias of −0.9‰ and −36% for ∆17O(NO3−) and [Ox,major] (≡ [O3] + [NO2] + [p‐NO3−]), respectively, while overestimating the nitrogen oxidation ratio ([NO3−]/([NO3−] + [NO2])) by +0.12 in intense haze. The discrepancies become larger in more intense haze. We attribute the model biases to an overestimate of NO2‐uptake on aerosols and an underestimate in wintertime O3concentrations. Our findings highlight a need to address uncertainties related to heterogeneous chemistry of NO2in air‐quality models. The combined assessment of observations and model results suggest that N2O5uptake in aerosols and clouds is the dominant nitrate‐production pathway in wintertime Beijing, but its rate is limited by ozone under high‐NOx‐high‐PM2.5conditions. Nitrate production rates may continue to increase as long as [O3] increases despite reduction in [NOx], creating a negative feedback that reduces the effectiveness of air pollution mitigation.
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Intraurban NO 2 hotspot detection across multiple air quality products
Abstract High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of ‘gold-standard’ regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO2hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-OrdGi*statistic to identify hotspots of NO2and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights.
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- Award ID(s):
- 2239834
- PAR ID:
- 10464253
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 18
- Issue:
- 10
- ISSN:
- 1748-9326
- Page Range / eLocation ID:
- Article No. 104010
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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