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NASA NTRS2023

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗

NASA NTRS2017

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, approximately 70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

MAIAC AO↗

NASA NTRS2017

PM2.5 Variation Related to AOD and Meteorological Conditions on Diurnal Scale in Beijing, China

PM2.5 (particulate matter with diameter less than 2.5um) is one of the major air pollutants in many regions around the globe. Column integrated aerosol optical depth (AOD) measurement provides a potential way to estimate surface PM2.5 concentration from the remote sensing technics. In this analysis, we compiled hourly measurements of surface PM2.5 and AOD made in Beijing, China and meteorological conditions provided by NASA Global Modeling and Assimilation Office's Modern-Era Retrospective Analysis for Research and Applications (MERRA) product in recent years. On daily basis, hourly PM2.5 and AOD show positive correlation, i.e. correlation coefficient (R) is higher than 0.5 on about 50% of days. We apply multi-variant analysis to this dataset and estimate the magnitude of AOD variance that can be explained by or related to PM2.5 and relevant meteorological conditions changes.

PM2.5↗

DOE OSTI2021

Evaluation of regional transport of PM2.5 during severe atmospheric pollution episodes in the western Yangtze River Delta, China

During winter 2018, the 16 prefecture-level cities in Anhui Province, Western Yangtze River Delta region, China had very high PM2.5 concentrations and prolonged pollution days. The impact of regional transport in the formation, accumulation, as well as dispersion of fine particulate matter (PM2.5) in Anhui Province was very significant. This study quantified and analyzed the vertical transport of PM2.5 in three major cities (Hefei, Fuyang, and Suzhou) of Anhui Province in January and July 2018 using the Weather Research and Forecasting (WRF) model coupled with the Community Multiscale Air Quality (CMAQ) model. The results of the inter-regional transport of PM2.5 revealed the dominant transport pathways for the three cities. The flux mainly flowed into Fuyang from Henan (2.23 and 1.42 kt/day in January and July, respectively) and Bozhou (1.96 and 1.21 kt/day in January and July, respectively), while the main flux from Fuyang flowed into Henan (-2.15 kt/day) and Lu'an (-1.91 kt/day) in January and Henan (-0.34 kt/day) and Bozhou (-0.29 kt/day) in July. In addition, the dominant transport pathways and the heights at which they occurred were identified: the northwest-southeast and northeast-south pathways in both winter and summer at both lower (?300 m) and higher (=300 m) levels for Fuyang; the northwest-south and northeast-southwest pathways in winter (at both lower and upper levels) and northwest-east and northeast-southwest pathways in summer at lower and upper levels for Hefei; and the northwest-southeast and northeast-south pathways in both winter (from 50 m up to the top level) and summer (between 100 and 300 m) for Suzhou. Furthermore, the intensities of daily PM2.5 transport fluxes in Fuyang during the atmospheric pollution episode (APE1) were stronger than the monthly average. These results show that joint emission controls across multiple cities along the identified pathways are urgently needed to reduce winter episodes.

Sulaymon, Ishaq D.↗

NASA NTRS2017

Daily Kilometer-Scale MODIS Satellite Maps of PM2.5 Describe Wintertime Episodes

The San Joaquin Valley (SJV) suffers from severe health-endangering episodes of PM2.5 aerosol loadings in wintertime; episodes last approximately 5 days and differ in geographical distribution and composition. PM2.5 stations are scattered; consequently the use of remote sensing to map variable regional patterns of these varying respirable aerosol concentrations is desirable. High-precision AOT retrievals can capture column particulate loading. However,PM2.5 mapping is challenging due to several reasons: particularly thin mixed layers (ML) and thus relatively low aerosol optical thickness (AOT) close to current measurement limits, variable and a typical composition of the aerosols, and complex surface bidirectional reflectance. However, the West does present some advantages in analysis. Air basins are isolated from long-distance transport, and experience predominant strong meteorological subsidence. Thus these Western basin regions have fewer problematic cases of overriding aerosol layers detached from the surface. To counter such local overriding, Chu et al. have described an approach for the Eastern US, and He et al have described a synoptic classification approach useful in Shanghai. The Bay Area Air Quality Management District (BAAQMD) expands our experience with the use of AOT, with lower PM2.5 and several isolated sub-basins. We have prepared daily maps of episodes in each region. We present also a sequence of increasingly detailed statistical models, AOT initially appears to contribute little information; however, inclusion of weather information reveals its utility. Lyapustin and Wang's MultiAngle Implementation of Atmospheric Correction (MAIAC) retrieval for AOT provided the most useful operational remote sensing information for these regions. It provides high (1-km) spatial resolution maps and a high percentage of availability. Empirical regression methods have found that random effects regression models (aka mixed effects models, ME) employing AOT provide good estimates of ground PM2.5 concentrations.Here, we attempt to extend these methods and evaluate the usefulness of AOT with greater physical analysis, based on DISCOVER-AQ4 experience.

Chatfield, Robert B.↗

NASA NTRS2014

Regional Characteristics of the Relationship Between Columnar AOD and Surface PM2.5: Application of Lidar Aerosol Extinction Profiles over Baltimore-Washington Corridor During DISCOVER-AQ

The first field campaign of DISCOVER-AQ (Deriving Information on Surface conditions from COlumn and VERtically resolved observations relevant to Air Quality) took place in July 2011 over Baltimore-Washington Corridor (BWC). A suite of airborne remote sensing and in-situ sensors was deployed along with ground networks for mapping vertical and horizontal distribution of aerosols. Previous researches were based on a single lidar station because of the lack of regional coverage. This study uses the unique airborne HSRL (High Spectral Resolution Lidar) data to baseline PM2.5 (particulate matter of aerodynamic diameter less than 2.5 μm) estimates and applies to regional air quality with satellite AOD (Aerosol Optical Depth) retrievals over BWC (∼6500 sq. km). The linear approximation takes into account aerosols aloft above AML (Aerosol Mixing Layer) by normalizing AOD with haze layer height (i.e., AOD/HLH). The estimated PM2.5 mass concentrations by HSRL AOD/HLH are shown within 2 RMSE (Root Mean Square Error ∼9.6 μg/cu. m) with correlation ∼0.88 with the observed over BWC. Similar statistics are shown when applying HLH data from a single location over the distance of 100 km. In other words, a single lidar is feasible to cover the range of 100 km with expected uncertainties. The employment of MPLNET-AERONET (MicroPulse Lidar NETwork - AErosol RObotic NETwork) measurements at NASA GSFC produces similar statistics of PM2.5 estimates as those derived by HSRL. The synergy of active and passive remote sensing aerosol measurements provides the foundation for satellite application of air quality on a daily basis. For the optimal range of 10 km, the MODIS-estimated PM2.5 values are found satisfactory at 27 (out of 36) sunphotometer locations with mean RMSE of 1.6-3.3 μg/cu. m relative to PM2.5 estimated by sunphotometers. The remaining 6 of 8 marginal sites are found in the coastal zone, for which associated large RMSE values ∼4.5-7.8 μg/cu. m are most likely due to overestimated AOD because of water-contaminated pixels.

DISCOVER-AQ↗

NASA NTRS2016

Maps Suggest Transport and Source Processes of PM2.5 at 1 km x 1 km for the Whole San Joaquin Valley, Winter 2011 (Generalizations from DISCOVER-AQ)

We present interpreted data analysis using MAIAC (Multiangle implementation of Atmospheric Correction) retrievals and appropriate RAPid Update Cycle (RAP) meteorology to map respirable aerosol (PM2.5) for the period January and February, 2011. The San Joaquin Valley is one of the unhealthiest regions in the USA for PM2.5 and related morbidity. The methodology evaluated can be used for the entire moderate-resolution imaging spectrometer (MODIS, VIIRS) data record. Other difficult areas of the West: Riverside, CA, Salt Lake City, UT, and Doa Ana County, NM share similar difficulties and solutions. The maps of boundary layer depth for 1116 hr local time from RAP allows us to interpret aerosol optical thickness as a concentration of particles in a nearly well-mixed box capped by clean air. That mixing is demonstrated by DISCOVER-AQ data and afternoon samples from the airborne measurements, P3B (on-board) and B200 (HSRL2 lidar). This data and the PM2.5 gathered at the deployment sites allowed us to estimate and then evaluate consistency and daily variation of the AOT to PM2.5 relationship. Mixed-effects modeling allowed a refinement of that relation from day to day; RAP mixed layers explained the success of previous mixed-effects modeling. Compositional, size-distribution, and MODIS angle-of-regard effects seem to describe the need for residual daily correction beyond ML depth.We report on an extension method to the entire San Joaquin Valley for all days with MODIS imagery using the permanent PM2.5 stations, evaluated for representativeness. Resulting map movies show distinct sources, particularly Interstate-5 (at approx. 1km x 1km resolution) and the broader Bakersfield area. Accompanying winds suggest transport effects and variable pathways of pollution cleanout. Such estimates should allow morbiditymortality studies. They should be also useful for actual model assimilations, where composition and sources are uncertain. We conclude with a description of new work to extend these insights to similar regions, e.g. interior valleys of California, the Po Valley, the Mediterranean litoral, and the Ganges Plain.This work show generalizable use of remote sensing, a major goal of DISCOVER-AQ, Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality.

Chatfield, R.↗

DOE OSTI2023

Observational Study Reveals Inverse Relationship: Rising PM2.5 Concentrations Linked to Decreasing Muon Flux

Muon flux, the rate of muons reaching Earth from the atmosphere, is impacted by various factors such as air pressure, temperature, and humidity. However, the influence of concentrations of PM2.5 (particulate matter with diameters 2.5 mm or smaller) on muon detection rates remains unexplored. During the summer of 2023, smoke from Canadian wildfires (containing significant amounts of particulate matter) blew over regions in the Northern US, introducing huge fluctuations in PM2.5 concentrations, thus inspiring this experiment to investigate the correlation of PM2.5 concentrations and muon rates. To investigate the correlation, muon collision rates were measured and analyzed alongside PM2.5 concentration data over the periods of both light and heavy smoke. Other confounding variables, including temperature, humidity, and atmospheric pressure, were also considered. The results reveal a statistically significant inverse correlation between muon flux and PM2.5 concentrations, indicating that particulate matter has an impact on the rate of muons reaching the earth’s surface.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Observational Study Reveals Inverse Relationship: Rising PM2.5 Concentrations Linked to Decreasing Muon Flux

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Experimental and Modeled Assessment of Interventions to Reduce PM2.5 in a Residence during a Wildfire Event

Increasingly large and frequent wildfires affect air quality even indoors by emitting and dispersing fine/ultrafine particulate matter known to pose health risks to residents. With this health threat, we are working to help the building science community develop simplified tools that may be used to estimate impacts to large numbers of homes based on high-level housing characteristics. In addition to reviewing literature sources, we performed an experiment to evaluate interventions to mitigate degraded indoor air quality. We instrumented one residence for one week during an extreme wildfire event in the Pacific Northwest. Outdoor ambient concentrations of PM2.5 reached historic levels, sustained at over 200 μg/m3 for multiple days. Outdoor and indoor PM2.5 were monitored, and data regarding building characteristics, infiltration, and mechanical system operation were gathered to be consistent with the type of information commonly known for residential energy models. Two conditions were studied: a high-capture minimum efficiency rated value (MERV 13) filter integrated into a central forced air (CFA) system, and a CFA with MERV 13 filtration operating with a portable air cleaner (PAC). With intermittent CFA operation and no PAC, indoor corrected concentrations of PM2.5 reached 280 μg/m3, and indoor/outdoor (I/O) ratios reached a mean of 0.55. The measured I/O ratio was reduced to a mean of 0.22 when both intermittent CFA and the PAC were in operation. Data gathered from the test home were used in a modeling exercise to assess expected I/O ratios from both interventions. The mean modeled I/O ratio for the CFA with an MERV 13 filter was 0.48, and 0.28 when the PAC was added. The model overpredicted the MERV 13 performance and underpredicted the CFA with an MERV 13 filter plus a PAC, though both conditions were predicted within 0.15 standard deviation. The results illustrate the ways that models can be used to estimate indoor PM2.5 concentrations in residences during extreme wildfire smoke events.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

NASA NTRS2016

Retrieval of Daily Maps of PM2.5 Aerosol in the Problematic California Valleys: Bright, Speckled Reflectances, Thin AOT, but High Pollution

The San Joaquin Valley suffers from severe episodes of respirable aerosol (PM2.5) in wintertime.We provide maps of aerosol episodes using daily snapshots of PM2.5 and its changing features despite numerous difficulties inherent to sampling the region. Linear relationships relating aerosol optical thickness (AOT) to PM2.5 give an explained variance of approximately 3.The GEO-CAPE mission has as a goal the provision of relevant measures of respirable aerosol to the community,but has not formulated a science goal beyond the limited goal of retrieval of AOT, bringing the usefulness of GEO-CAPE into doubt.Our special focus was on the DISCOVER-AQ period, Jan-Feb 2013, which had many supporting measurements.Both high pollution and retrieval difficulties tend to occur in many Mediterranean agricultural regions like the San Joauin. One difficulty is the relatively bright surfaces with considerable exposed soil. NASAs MAIAC and MODIS Deep Blue retrieval techniques are shown to have considerable skill even at low aerosol optical thickness (AOT) values, as evaluated by concurrent AERONET sunphotometer measurements.More significantly, these AOT values can correspond to high daytime PM2.5 since aerosol mixed layer depth is thin and variable, 200m 600 m. The thin layers derive from typical subsidence of dry air between more stormy periods. This situation provides an advantage: water vapor column is also almost completely limited to a similar mixed layer depth, and can thus serve as a measure of aerosol dilution.Using the MAIAC Water Vapor Column:In order to make the maps below, we used the MAIAC data but subtracted partial water-vapor columns estimated from MERRA Reanalysis Data availabe from the GSFC GMAO using kriging. We did not use the mixed-layer estimates from MERRA, since such analyses were found problematic during our forecasting exercises for DISCOVER-AQ. Observations from the aircraft soundings suggested that this overlying moisture was mostly due to larger scale flows, not ML venting.However, the specific humidity at the surface and a nearly well-mixed ML was analyzed by kriging from the surface network (MesoWest, University of Utah). These were thought to be truer, uninfluenced by physical process modeling that combines with data observations. (TBD: How different are they?) Procedure: Subtract overlying partial water columns from MAIAC column water and divide this by a surface value of water vapor. (MAIAC column is expressed in cm of water, i.e., water vapor at surface conditions.This method appears to bring out useful details in the distribution of submicron particles in the very problematic Wintertime San Joaquin Valley, and allow analysis of pollution episodes throughout the valley, rather than long-term averages.

san joaquin valley↗

NASA NTRS2021

First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors

Estimates of air pollution mortality in sub-Saharan Africa are limited by a lack of surface observations of fine particulate matter (PM2.5). Despite being large metropolises, Kinshasa, Democratic Republic of the Congo (DRC), population 14.3 million, and Brazzaville, Republic of the Congo (ROC), population 2.4 million, have no reference air pollution monitors at the time of writing. Recently, a few reference monitors have been deployed in other parts of sub-Saharan Africa, including Kampala, Uganda. A low-cost PurpleAir PM2.5 monitor was collocated next to the Kampala US Embassy BAM-1020 (Met One Beta Attenuation Monitor) starting in August 2019. Raw PurpleAir data are strongly correlated with the BAM (r(exp 2) = 0.88), but have a mean absolute error of approximately 14 μg/cu.m. Two calibration models, multiple linear regression and a random forest approach, decrease mean absolute error (MAE) from 14.3 μg/cu.m to 3.4 µg/cu.m or less and improve the the r(exp 2) from 0.88 to 0.96. Given )the similarity in climate and emissions in Kampala, we apply the collocated field correction factors to four PurpleAir sensors in Kinshasa, DRC and one in neighboring Brazzaville, ROC deployed beginning April 2018. Annual average PM2.5 for 2019 in Kinshasa is estimated at 43.5 µg/cu.m, more than 4 times higher than WHO Interim Target 1 of 10 µg/cu.m. Surface PM2.5 and aerosol optical depth were each about 40% lower during the 2020 COVID19 lockdown period compared to the same time period in 2019, which cannot be explained by changes in meteorology or wildfire emissions alone. Our results highlight the need for clean air solutions implementation in the Congo.

Celeste McFarlane↗

NASA NTRS2021

Application of Gaussian Mixture Regression for the Correction of Low Cost PM2.5 Monitoring Data in Accra, Ghana

Low-cost sensors (LCSs) for air quality monitoring have enormous potential to improve air quality data coverage in resource-limited parts of the world such as sub-Saharan Africa. LCSs, however, are affected by environment and source conditions. To establish high-quality data, LCSs must be collocated and calibrated with reference grade PM2.5 monitors. From March 2020, a low-cost PurpleAir PM2.5 monitor was collocated with a Met One Beta Attenuation Monitor 1020 in Accra, Ghana. While previous studies have shown that multiple linear regression (MLR) and random forest regression (RF) can improve accuracy and correlation between PurpleAir and reference data, MLR and RF yielded suboptimal improvement in the Accra collocation (R2 = 0.81 and R2 = 0.81, respectively). We present the first application of Gaussian mixture regression (GMR) to air quality data calibration and demonstrate improvement over traditional methods by increasing the collocated PM2.5 correlation and accuracy to R2 = 0.88 and MAE = 2.2 μg/cu. m. Gaussian mixture models (GMMs) are a probability density estimator and clustering method from which nonlinear regressions that tolerate missing inputs can be derived. We find that even when given missing inputs, GMR provides better correlation than MLR and RF performed with complete data. GMR also allows us to estimate calibration certainty. When evaluated, 95% confidence intervals agreed with reference PM2.5 data 96% of the time, suggesting that the model accurately assesses its own confidence. Additionally, clustering within the GMM is consistent with climate characteristics, providing confidence that the calibration approach can learn underlying relationships in data.

Sensors↗

NASA NTRS2024

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Humidity↗

DOE OSTI2020

Annual PM2.5 and cardiovascular mortality rate data: Trends modified by county socioeconomic status in 2,132 US counties

This article contains data on county-level socioeconomic status for 2132 US counties and each county's average annual cardiovascular mortality rate (CMR) and fine particulate matter (PM2.5) concentration for 21 years (1990e2010). County CMR, PM2.5, and socioeconomic data were obtained from the US National Center for Health Statistics, US Environmental Protection Agency's Community Multiscale Air Quality modeling system, and the US Census, respectively. Annual socioeconomic indices were created using seven county-level measures from the 1990, 2000, and 2010 US Census using factor analysis. Quintiles of this index were used to generate categories of county socioeconomic status. This national data set contains data for annual PM2.5 and CMR changes over a time-period when there was a significant reduction in US air pollutants (following the enactment of the 1970 Clean Air Act). These data are associated with the article “The contribution of improved air quality to reduced cardiovascular mortality: Declines in socioeconomic differences over time” [1]. Data are stored in a comma separated value format and can be downloaded from the USEPA ScienceHub data repository (https://doi.org/10.23719/ 1506014).

Socioeconomic status↗

DOE OSTI2020

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980–2019 using a machine learning approach

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM 2.5 (particulate matter less than 2.5 µm in diameter) concentrations. This study constructs a gridded near-surface PM 2.5 concentration dataset across China covering 1980–2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM2.5 concentrations are in excellent agreement with ground measurements during 2015–2019, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, both emissions and meteorological conditions are also essential factors affecting PM2.5 predictions. From 1980 to 2014, the model-predicted PM2.5 concentrations increased constantly with the maximum growth rate of 5–10 µg/m3/decade over eastern China. Due to the clean air actions, PM 2.5 concentrations have decreased effectively at a rate over 50 µg/m 3 /decade in the North China Plain and 20–50 µg/m 3 /decade over eastern China during 2014–2019. The newly generated dataset of 1-degree gridded PM 2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Comparison of Current and Future PM2.5 Air Quality in China Under CMIP6 and DPEC Emission Scenarios

The latest scenarios for the sixth Coupled Model Intercomparison Project (CMIP6) provide opportunities to explore future climate and air pollution mitigation pathways with socioeconomic features. However, to what extent inadequate consideration of region-specific policies in the CMIP6 scenarios would affect regional air quality projections is still not clear. Here, we simulated China’s future PM2.5 concentrations and compositions with local policy-based scenarios and the original CMIP6 datasets, for the first time estimating future air quality differences induced by the lack of local pollution control policies in the CMIP6 scenarios. We found that the CMIP6-driven projections overestimate major chemical aerosols by 10% – 70% in 2015 and fail to capture the rapid PM2.5 concentration decline during 2015–2019. Near-term differences further lead to 43% – 49% (5 – 9 µg/m3) PM2.5 overpredictions in 2050 in CMIP6 scenarios compared with policy-based mitigation scenarios. Our study demonstrates the indispensable influences of local policies on regional analyses and suggests that the research community should incorporate more region-specific information in future scenario designs.

Cheng, Jing↗

NASA NTRS2005

Possibilities and Challenges in Using Satellite Data for PM2.5 Forecasts

Satellite remote sensing has brought our observation of the earth's atmosphere into a new era, and remote sensing capability could lead to a quantum leap in our ability of air quality monitoring and prediction. In terms of aerosols, the most common quantity from satellite retrieval is the atmospheric column aerosol optical thickness (AOT), and the most common quantity indicating air quality at the surface is the concentration of PM2.5. w e present here the relationship between the column AOT and the surface PM2.5 from a global aerosol model GOCART and from satellite and surface measurements. We will discuss the possibilities and challenges in using satellite data for PM2.5 forecasts, and if model-satellite assimilation can improve the forecast quality.

Chin, Mian↗