This study estimated long-term average ambient NO2 concentrations using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric NO2 data and land use information at the spatial resolution of 500 m in California for the years 2018-2019. Our satellite-land use regression model demonstrated reasonably high predictive power with cross-validation (CV) R2 = 0.76, mean absolute error (MAE) = 1.95 ppb, and root mean squared error (RMSE) = 2.51 ppb in a comparison between measured and estimated NO2 concentrations. Exploiting the high-resolution NO2 estimates, we further investigated the representativeness of ground NO2 monitors for population exposures and exam-ined the spatial variation of NO2 in relation to parcel-level property data for exposure attributions. The ground NO2 monitors were the most representative of population exposures in Los Angeles and San Diego counties, supported by population-weighted average NO2 concentrations (satellite-derived estimations) similar to arithmetic average NO2 concentrations (ground measurements). On the contrary, the exposure assessment using the ground monitors was the least representative and protective in Humboldt, San Luis Obispo, and Yolo counties with population -weighted average NO2 greater than arithmetic average NO2 by 82.2 % (1.85 ppb), 67.1 % (1.89 ppb), and 58.2 % (2.48 ppb), respectively. In a case study of LA County, we identified comparatively high NO2 concentrations for the property types of food processing facilities and high-density residential complexes (such as high-rise apartments and apartments). This finding provides evidence that these emerging sources may be crucial to mitigate cumulative NO2 exposures and subsequent health risks from a regulatory perspective.

<p>This study estimated ambient long-term average NO<sub>2</sub> concentrations using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric column NO<sub>2</sub> data and land use parameters in California, U.S. for the years 2018–2019. Exploiting unprecedentedly high spatial resolution of TROPOMI NO<sub>2</sub> (3.5×7 km prior to August 6, 2019 and 3.5×5.5 km thereafter) and the point statistics function implemented in ArcGIS (Environmental Systems Research Institute, ESRI), the NO<sub>2</sub> concentration estimates were downscaled to 500 m, which enabled the neighborhood-scale exposure assessment of ambient NO<sub>2</sub>. Our satellite-land use hybrid regression model demonstrated cross-validation R<sup>2</sup>= 0.76, mean absolute error (MAE)= 1.95 ppb, and root mean squared error (RMSE)= 2.51 ppb in a comparison between site-specific average measured and estimated NO<sub>2</sub> concentrations. These high-resolution NO<sub>2</sub> concentration estimates enhanced the capability of human exposure assessment, enabling (1) the evaluation of ground NO<sub>2</sub> monitors to represent population exposures and (2) the attribution of micro-level NO<sub>2</sub> exposures. When measured NO<sub>2</sub> concentrations were compared to population-weighted NO<sub>2</sub> concentrations, calculated by using the satellite-based NO<sub>2</sub> estimates, in each county, the differences in NO<sub>2</sub> concentrations (i.e., population-weighted average NO<sub>2</sub> – arithmetic average NO<sub>2</sub> measurements) ranged from -38.6% (San Bernardino) to 82.2% (Humboldt). Though both negative and positive differences represented exposure errors without considering the spatial co-variations of NO<sub>2</sub> and populations, monitor-based NO<sub>2</sub> higher than the population-weighted NO<sub>2</sub> demonstrated the overestimation of population NO<sub>2</sub> exposures and was at least protective with current NO<sub>2</sub> monitoring locations in the counties. However, the opposite was detrimental, while underestimating population NO<sub>2</sub> exposures and likely not motivating NO<sub>2</sub> mitigation efforts. In addition, the high-resolution NO<sub>2</sub> estimates were further overlaid with parcel-level property data in Los Angeles County to attribute the spatial variation of NO<sub>2</sub> exposures to that of the property types. The micro-level NO<sub>2</sub> hotspots were identified at high-density residential complexes such as (high-rise) apartments. When the traffic impacts on NO<sub>2</sub> were adjusted, the NO<sub>2</sub> hotspots at the residential complexes still remained. This finding may suggest residential complexes as an emerging source type of NO<sub>2</sub> due to emissions from boilers (space heating and hot water) and other indoor-to-outdoor ventilation systems.</p>

The present study summarizes two decades (2000?2019) of climatology and trends in aerosol loading and optical properties using a high spatial resolution data obtained from NASA?s MODIS MAIAC and MISR aerosol products supplemented by moderate resolution aerosol data from OMI sensor over South Asia (SA). MISR AOD showed good agreement against AERONET AOD with 68.68% of the retrievals falling within the expected error and high Pearson?s correlation coefficient (R = 0.83). The 20 years geometric mean of MAIAC and MISR AOD revealed higher loading of aerosols over the Indo-Gangetic Plain (IGP) and Eastern coast of India by 30% to 44% compared to the mean AOD over the entire SA. The highest mean AOD under cloud-free conditions was noted during monsoon season, followed by pre-monsoon, post-monsoon, and winter. The high contribution of coarsemode AOD (cAOD) mainly from natural aerosol emission and small-mode AOD (sAOD) from local anthropogenic emissions are the main driver to high AOD in monsoon and pre-monsoon seasons. Besides, the presence of high humidity during the monsoon season favors the hygroscopic growth of the particles and leads to higher AOD values over SA. The high spatial resolutions of MODIS/MAIAC and MISR aerosol products enabled the identification of previously unobserved aerosol hotspots over Bihar, West Bengal, and the eastern Indian coastal state of Odisha, which is mainly dominated by small aerosol particles. The contributions of smaller aerosol particles to the total aerosol loading were found to be higher during post-monsoon and winter over most states in India, Nepal, and Bangladesh. In contrast, the contribution of coarser particles was higher over Pakistan during premonsoon and monsoon seasons. Smaller particles were predominantly retrieved over the Indian states dominated by mining industries, including Jharkhand and Odisha. A typical dominance of absorbing carbonaceous aerosols was also noted over the northwestern region of IGP during post-monsoon, which otherwise was mainly affected by mixed dust aerosols and carbonaceous aerosols in pre-monsoon and monsoon seasons. A statistically significant positive temporal trend in AOD was observed for the whole study period, over most of the SA region, which was influenced by the increase in small particles over India and Bangladesh. Urban/industrial weakly absorbing aerosols were found to be the main contributor to a similarly positive trend over Central India and East coast Indian states. Overall, recent advancements in high spatial resolution satellite-based aerosol optical properties showed good potential to identify the aerosol hotspots and constrain aerosol types across a highly polluted SA region.

The atmospheric products of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm include column water vapor (CWV) at a 1 km resolution, derived from daily overpasses of NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Aqua and Terra satellites. We have recently shown that machine learning using extreme gradient boosting (XGBoost) can improve the estimation of MAIAC aerosol optical depth (AOD). Although MAIAC CWV is generally well validated (Pearson’s R > 0.97 versus CWV from AERONET sun photometers), it has not yet been assessed whether machine-learning approaches can further improve CWV. Using a novel spatiotemporal cross-validation approach to avoid overfitting, our XGBoost model, with nine features derived from land use terms, date, and ancillary variables from the MAIAC retrieval, quantifies and can correct a substantial portion of measurement error relative to collocated measurements at AERONET sites (26.9 % and 16.5 % decrease in root mean square error (RMSE) for Terra and Aqua datasets, respectively) in the Northeastern USA, 2000– 2015. We use machine-learning interpretation tools to illustrate complex patterns of measurement error and describe a positive bias in MAIAC Terra CWV worsening in recent summertime conditions. We validate our predictive model on MAIAC CWV estimates at independent stations from the SuomiNet GPS network where our corrections decrease the RMSE by 19.7 % and 9.5 % for Terra and Aqua MAIAC CWV. Empirically correcting for measurement error with machine-learning algorithms is a postprocessing opportunity to improve satellite-derived CWV data for Earth science and remote sensing applications.

Short-term air pollution episodes motivate improved understanding of the association between air pollution and acute morbidity and mortality episodes, and triggers required mitigation plans. A variety of methods have been employed to estimate exposure to air pollution episodes, including GIS-based dispersion models, interpolation between sparse monitoring sites, land-use regression models, optimization models, line- or area-dispersion plume models, and models using information from imaging satellites, often including land-use and meteorological variables. There has been increasing use of satellite-borne aerosol products for assessing short-term air quality events. They provide better spatial coverage, but currently at the price of low temporal coverage and rather crude spatial resolution. This is a brief review on using satellite data for modeling short-term air quality and pollution events. The review can be pursued as a practical guide for modeling air quality with satellite-based products, as it includes important questions that should be considered in both the study design as well as the model development stages. Progress in this field is detailed and includes published models and their use in environmental and health studies. Both current and future satellite-borne capabilities are covered. It also provides links to access and download relevant datasets and some example R code for data processing and modeling.

Studies of emission factors from biomass burning using aircraft data complement the results of lab studies and extend them to conditions of immense hot conflagrations. A new theoretical development of plume theory for multiple tracers is developed after examining aircraft samples. We illustrate and discuss emissions relationships for 422 individual samples from many forest fire plumes in the Western USA. Samples are from two NASA investigations: ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) and SEAC4RS (Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys). This work provides sample-by-sample enhancement ratios (EnRs) for 23 gases and particulate properties. Many EnRs provide candidates for emission ratios (ERs, corresponding to the EnR at the source) when the origin and degree of transformation is understood. From these, emission factors (EFs) can be estimated, provided the fuel dry mass consumed is known or can be estimated using the carbon mass budget approach. This analysis requires understanding the interplay of mixing of the plume with surrounding air. Some initial examples emphasize that measured Ctot=CO2+CO in a fire plume does not necessarily describe the emissions of the total carbon liberated in the flames, Cburn. Rather, it represents Ctot=Cburn+Cbkgd, which includes possibly varying background concentrations for entrained air. Consequently, we present a simple theoretical description for plume entrainment for multiple tracers from the flame tops to hundreds of kilometers downwind and illustrate some intrinsic linear behaviors. The analysis suggests a mixed-effects regression emission technique (MERET), which can eliminate occasional strong biases associated with the commonly used normalized excess mixing ratio (NEMR) method. MERET splits Ctot to reveal Cburn by exploiting the fact that Cburn and all tracers respond linearly to dilution, while each tracer has consistent EnR behavior (slope of tracer concentration with respect to Cburn). The two effects are separable. Two or three or preferably more emission indicators are required as a minimum; here we used eight. In summary, MERET allows a fine spatial resolution (EnRs for individual observations) and comparison of similar plumes that are distant in time and space. Alkene ratios provide us with an approximate photochemical timescale. This allows discrimination and definition, by fire situation, of ERs, allowing us to estimate emission factors.

Very high spatially resolved satellite-derived ground-level concentrations of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) have multiple potential applications, especially in air quality modeling and epidemiological and climatological research. Satellite-derived aerosol optical depth (AOD) and columnar water vapor (CWV), meteorological parameters, and land use data were used as variables within the framework of a linear mixed effect model (LME) and a random forest (RF) model to predict daily ground-level concentrations of PM2.5 at 1 km × 1 km grid resolution across the Indo-Gangetic Plain (IGP) in South Asia. The RF model exhibited superior performance and higher accuracy compared with the LME model, with better cross-validated explained variance (R2 = 0.87) and lower relative prediction error (RPE = 24.5%). The RF model revealed improved performance metrics for increasing averaging periods, from daily to weekly, monthly, seasonal, and annual means, which supported its use in estimating PM2.5 exposure metrics across the IGP at varying temporal scales (i.e., both short and long terms). The RF-based PM2.5 estimates showed high PM2.5 levels over the middle and lower IGP, with the annual mean exceeding 110 μg/m3. As for seasons, winter was the most polluted season, while monsoon was the cleanest. Spatially, the middle and lower IGP showed poorer air quality compared to the upper IGP. In winter, the middle and lower IGP experienced very poor air quality, with mean PM2.5 concentrations of >170 μg/m3.

We examined windblown dust within the Imperial Valley (CA) during strong springtime west-southwesterly (WSW) wind events. Analysis of routine agency meteorological and ambient particulate matter (PM) measurements identified 165 high WSW wind events between March and June 2013 to 2019. The PM concentrations over these days are higher at northern valley monitoring sites, with daily PM mass concentration of particles less than 10 micrometers aerodynamic diameter (PM10) at these sites commonly greater than 100 μg/m3 and reaching around 400 μg/m3, and daily PM mass concentration of particles less than 2.5 micrometers aerodynamic diameter (PM2.5) commonly greater than 20 μg/m3 and reaching around 60 μg/m3. A detailed analysis utilizing 1 km resolution multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), Identifying Violations Affecting Neighborhoods (IVAN) low-cost PM2.5 measurements and 500 m resolution sediment supply fields alongside routine ground PM observations identified an area of high AOD/PM during WSW events spanning the northwestern valley encompassing the Brawley/Westmorland through the Niland area. This area shows up most clearly once the average PM10 at northern valley routine sites during WSW events exceeds 100 μg/m3. The area is consistent with high soil sediment supply in the northwestern valley and upwind desert, suggesting local sources are primarily responsible. On the basis of this study, MAIAC AOD appears able to identify localized high PM areas during windblown dust events provided the PM levels are high enough. The use of the IVAN data in this study illustrates how a citizen science effort to collect more spatially refined air quality concentration data can help pinpoint episodic pollution patterns and possible sources important for PM exposure and adverse health effects.

The use of satellite aerosol optical thickness (AOT) from imaging spectrometers has been successful in quantifying and mapping high-PM2.5 (particulate matter with a mass <2.5 µm diameter) episodes for pollution abatement and health studies. However, some regions have high PM2.5 but poor estimation success. The challenges in using AOT from imaging spectrometers to characterize PM2.5 worldwide was especially evident in the wintertime San Joaquin Valley (SJV). The SJV's attendant difficulties of high-albedo surfaces and very shallow, variable vertical mixing also occur in other significantly polluted regions around the world. We report on more accurate PM2.5 maps (where cloudiness permits) for the whole winter period in the SJV (19 November 2012–18 February 2013). Intensive measurements by including NASA aircraft were made for several weeks in that winter, the DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) California mission. We found success with a relatively simple method based on calibration and checking with surface monitors and a characterization of vertical mixing, and incorporating specific understanding of the region's climatology. We estimate PM2.5 to within ∼7 µg m−3 root mean square error (RMSE) and with R values of ∼0.9, based on remotely sensed multi-angle implementation of atmospheric correction (MAIAC) observations, and certain further work will improve that accuracy. Mapping is at 1 km resolution. This allows a time sequence of mapped aerosols at 1 km for cloud-free days. We describe our technique as a “static estimation.” Estimation procedures like this one, not dependent on well-mapped source strengths or on transport error, should help full source-driven simulations by deconstructing processes. They also provide a rapid method to create a long-term climatology. Essential features of the technique are (a) daily calibration of the AOT to PM2.5 using available surface monitors, and (b) characterization of mixed layer dilution using column water vapor (CWV, otherwise “precipitable water”). We noted that on multi-day timescales both water vapor and particles share near-surface sources and both fall to very low values with altitude; indeed, both are largely removed by precipitation. The existence of layers of H2O or aerosol not within the mixed layer adds complexity, but mixed-effects statistical regression captures essential proportionality of PM2.5 and the ratio variable (AOT ∕ CWV). Accuracy is much higher than previous statistical models and can be extended to the whole Aqua satellite data record. The maps and time series we show suggest a repeated pattern for large valleys like the SJV – progressive stabilization of the mixing height after frontal passages: PM2.5 is somewhat more determined by day-by-day changes in mixing than it is by the progressive accumulation of pollutants (revealed as increasing AOT).

Deserts are fragile and highly sensitive ecosystems that increasingly are affected by upwind urban areas and industrial activities. The Los Angeles Basin (LAB) contributes to poor air quality in downwind deserts including the Mojave Desert. Few studies have investigated potential air pollution inputs to the Mojave, whose fragile ecosystem includes endangered plant and animal species. Data were collected on 19 August 2015 by a mobile air quality laboratory, AMOG (AutoMObile trace Gas) Surveyor, that observed inputs can arise from the LAB as well as the San Joaquin Valley (SJV), California. The campaign used a strong methane (CH4) plume as a tracer for the downwind fate of emissions from Bakersfield area petroleum production and also measured ozone (O-3). Additional in situ concurrent airborne GHG and O-3 data were collected by AJAX - Alpha Jet Atmospheric eXperiment. Both AMOG and AJAX measure winds. Mojave Desert air quality was very poor (visibility 4 km). Based on the winds, an additional source was inferred beyond the LAB and SJV Basins. Numerical transport modeling and analysis of aerosol lidar data collected the same day by the Cloud Profiling LiDAR onboard the Earth Research-2 stratospheric airplane demonstrated that fires in Northern California were responsible, with prevailing winds transporting air southwards along the eastern Sierra Nevada Range (Bishop Valley) to the Mojave. Whereas the southern and eastern Mojave are impacted by SJV and LAB outflow, the north Mojave generally avoids these inputs. This study shows it can be affected by even distant wildfires, which likely will increase in occurrence and intensity from climate change. Thus, regulatory efforts to reduce air quality impacts on the endangered Mojave ecosystem must include wildfires and also account for the significant differences between different regions of the Mojave. Currently, there is a paucity of studies, highlighting the critical need for field research.

et al., 2013) soon after the DISCOVER-AQ measurements in California.That work gave a syncretic overview of many trace species measurement besides water vapor which motivated our investigation, as described in Section 1.1.. First, there is an acknowledgement of the many authors contributing measurements.Second, there is an explanation of methods of estimating a characteristic mixed layer height as observed of airplane measurements.Third, there are graphs of vertical profiles normalized by average tracer concentration and also by the depth of the mixed layer.The commonalities and some differences of the first tracer shown (water vapor) and the last (particulate scattering).

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. Capsule: In comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage.