Kevin Civerolo

Estimating tropospheric ozone (O₃) production from observations is challenging but possible given the close coupling of O₃ with formaldehyde (HCHO) and nitrogen dioxide (NO₂), two remotely sensed air pollutants. The previous reliance on once‐daily satellite overpasses highlights the need to study diurnal changes and surface‐column relationships. Using surface observations, Pandora spectrometer retrievals, and a high‐resolution (1.33 km) air quality model (WRF‐CMAQ), we characterize diurnal patterns of HCHO and NO₂ at seven locations along an upwind‐downwind pathway through New York City during June–August 2018. Diurnal patterns of limited surface HCHO measurements suggest biogenic emission influence, while a bimodal surface NO₂ pattern indicates the impact of local anthropogenic nitrogen oxides emissions. Details of these patterns vary by site: an afternoon NO₂ spike at New Haven (CT) indicates traffic emissions, while a delayed daily HCHO peak at Westport (CT) relative to other sites likely reflects sea breeze dynamics. Peak column concentrations generally lag surface peaks by about four hours, occurring at 9–10 a.m. for morning NO₂ (from Pandora and WRF‐CMAQ) and around 4 p.m. for midday HCHO (from WRF‐CMAQ). TROPOMI overpass time at 1:30 p.m. misses peak column HCHO and NO₂ concentrations. A box model (F0AM) constrained with site‐level observations and WRF‐CMAQ fields indicates 1–9 ppb hr⁻¹ higher noontime local O₃ production rates on three sets of paired high‐ versus mid‐to‐low‐O₃ days. F0AM sensitivity analyses on these six days suggest a predominantly transitional O₃ formation regime at urban and downwind sites, differing at some sites from the NO_x‐saturated regime diagnosed for summertime average conditions via the weekday‐weekend effect.

Ambient exposure to fine particulate matter (PM_2.5) is one of the top global health concerns. We estimate the PM_2.5-related health benefits of emission reduction over New York State (NYS) from 2002 to 2012 using seven publicly available PM_2.5 products that include information from ground-based observations, remote sensing and chemical transport models. While these PM_2.5 products differ in spatial patterns, they show consistent decreases in PM_2.5 by 28%–37% from 2002 to 2012. We evaluate these products using two sets of independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program for an urban area, and the Saint Regis Mohawk Tribe Air Quality Program for a remote area. Inclusion of satellite remote sensing improves the representativeness of surface PM_2.5 in the remote area. Of the satellite-based products, only the statistical land use regression approach captures some of the spatial variability across New York City measured by NYCCAS. We estimate the PM_2.5-related mortality burden by applying an integrated exposure-response function to the different PM_2.5 products. The multi-product mean PM_2.5-related mortality burden over NYS decreased by 5660 deaths (67%) from 8410 (95% confidence interval (CI): 4570–12 400) deaths in 2002 to 2750 (CI: 700–5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM_2.5 mortality burden due to the choice of PM_2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function.

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM_2.5). We use a forward geophysical approach to derive ground-level PM_2.5 distributions from satellite AOD at 1 km² resolution for 2011 over the northeastern US by applying relationships between surface PM_2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km² horizontal resolution). Seasonal average satellite-derived PM_2.5 reveals more spatial detail and best captures observed surface PM_2.5 levels during summer. At the daily scale, however, satellite-derived PM_2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM_2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM_2.5 monitors, we show that uncertainties in modeled PM_2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM_2.5 evaluated at PM_2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM_2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM_2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM_2.5∕AOD lead to an error of 11 µg m⁻³ in daily satellite-derived PM_2.5, and uncertainties in satellite AOD lead to an error of 8 µg m⁻³. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM_2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM_2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM_2.5.

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM_2.5). We use a forward geophysical approach to derive ground-level PM_2.5 distributions from satellite AOD at 1 km² resolution for 2011 over the Northeast USA by applying relationships between surface PM_2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12 × 12 km² horizontal resolution). Seasonal average satellite-derived PM_2.5 reveals more spatial detail and best captures observed surface PM_2.5 levels during summer. At the daily scale, however, satellite-derived PM_2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly, to uncertainties in the relationship between surface PM_2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM_2.5 monitors, we show that uncertainties in modeled PM_2.5/AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM_2.5 evaluated at PM_2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM_2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM_2.5/AOD relationships. Overall, we estimate that uncertainties in the modeled PM_2.5/AOD lead to an error of 11 μg/m³ in daily satellite-derived PM_2.5, and uncertainties in satellite AOD lead to an error of 8 μg/m³. Using multi-platform ground, airborne and radiosonde measurements, we show that uncertainties of modeled PM_2.5/AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model uncertainties of relative humidity and aerosol vertical profile shape contribute some systematic biases. The parameterization of aerosol optical properties, which determines the mass-extinction efficiency, also contributes to random uncertainty, with the size distribution the largest source of uncertainty, and hygroscopicity of inorganic salt the second. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM_2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM_2.5.

Current implementations of federal and state regulations have relied heavily on regional-scale photo-chemical models which, however, reflect outdated...