Surface PM2 Research Articles

Assessment of sources and health impacts of PM10 in an urban environment over eastern coastal plain of India

Aerosol health impacts are gaining considerable attention from the scientific community. Our study's primary purpose was to identify the source regions of PM10 and quantify its long-term health effects at an eastern Indian urban site Bhubaneswar from July 2018 to June 2019. The seasonal average of PM10 surface observations displayed higher values in winter, followed by post-monsoon, summer and monsoon. Eastern India, Northern India, and South-East Asia emerged as potential source regions of PM10 contributors to the study location using source-receptor models. The health endpoints, (i) incidence of chronic bronchitis in adults, (ii) post neonatal infant mortality and, (iii) prevalence of bronchitis in children, were estimated employing the World Health Organization developed model, AirQ+. The attributable proportion, the excess number of attributable cases, and the excess number of attributable cases per 100,000 population for three endpoints showed the highest impact during the winter season. Winter impact is 3 to 5 times higher compared to the rest of other seasons. This study highlights the health impact of PM10 on the population of Bhubaneswar, especially during winter. Thus, it is imperative to give considerable focus to controlling and mitigating air pollution at the investigation location.

Joint Occurrence of Heavy PM2.5 Pollution Episodes and Persistent Foggy Days in Central East China

Although many severe pollution events in Central and East China have been analyzed in recent years, the heavy PM2.5 pollution episode happened on persistent foggy days from January 13 to 18, 2018 was unique, characterized by explosive increase and sharp decrease in PM2.5 (particles with kinetic equivalent diameter less than or equal to 2.5 microns) concentration. Based on hourly data of ground level meteorological parameters, PM2.5 data and CALIPSO-based (the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) aerosol data, combined with ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis data and radiosonde temperature profile, a comprehensive analysis was conducted to reveal the meteorological reasons for the evolution of the episode at horizontal and vertical scales. The PM2.5 concentration experienced four stages: a slow-increase phase, rapid-increase phase, rapid-decrease phase, and rebound phase. Results show that because Central and East China (CEC) were located at the back of a high-pressure system, humid southerly winds and near surface inversion (NSI) were responsible for the slow accumulation of pollutants. The rapid-increase phase was attributed to pollution transport at both ground level and in the lower troposphere because of weak cold air invasion. The significant subsidence at 500 hPa and 700 hPa intensified the NSI and led to dense fog. In that case, corresponding to the supersaturated atmosphere, the particles entered the fog droplets and were scavenged partly by deposition at night and were resuspended on the next day when the atmosphere was unsaturated. Our findings provide convincing evidence that surface PM2.5 rapid-decrease phase and the rebound phase were closely associated with dense fog process.

Duff burning from wildfires in a moist region: different impacts on PM<sub>2.5</sub> and ozone

Abstract. Wildfires can significantly impact air quality and human health. However, little is known about how different fuel bed components contribute to these impacts. This study investigates the air quality impacts of duff and peat consumption during wildfires in the southeastern United States, with a focus on the differing contributions of fine particulate matter less than 2.5 µm in size (PM2.5) and ozone (O3) to air quality episodes associated with the four largest wildfire events in the region during this century. The emissions of duff burning were estimated based on a field measurement of a 2016 southern Appalachian fire. The emissions from the burning of other fuels were obtained from the Fire INventory from NCAR (FINN). The air quality impacts were simulated using a three-dimensional regional air quality model. The results show the duff burning emitted PM2.5 comparable to the burning of the above-ground fuels. The simulated surface PM2.5 concentrations due to duff burning increased by 61.3 % locally over a region approximately 300 km within the fire site and by 21.3 % and 29.7 % in remote metro Atlanta and Charlotte during the 2016 southern Appalachian fires and by 131.9 % locally and by 17.7 % and 24.8 % in remote metro Orlando and Miami during the 2007 Okefenokee Fire. However, the simulated ozone impacts from the duff burning were negligible due to the small duff emission factors of ozone precursors such as NOx. This study suggests the need to improve the modeling of PM2.5 and the air quality, human health, and climate impacts of wildfires in moist ecosystems by including duff burning in global fire emission inventories.

Annual and early summer variability in WRF-CHEM simulated West African PM10 during 1960–2016

We examine annual PM10 concentrations across the Sahara and Sahel using the Weather, Research and Forecast (WRF) model with the GOCART dust module during 1960–2016 driven at the lateral boundaries with the NCEP reanalysis. We show that there is a simulated annual downward trend in surface PM10 concentrations in Senegal and Cabo Verde after the 1980s, which is similar to earlier findings. The analysis of simulated PM10 concentrations over the period of 1960–2016 during May, June and July (MJJ) suggests that there has been an increase in the vertical transport of dust over the Western Sahara which is then horizontally transported downstream at approximately 700 hPa for Cabo Verde. This suggests that dust transport to the Eastern Atlantic, which could be further transported downstream towards the Caribbean and United States, may have increased during the early 21st century.

Three-dimensional Structure Variation of PM2.5 During Cold Front Advance in Eastern China

Based on observational data and the WRF-Chem model, this study analyzed the large-scale air pollution in eastern China, which was caused by the weather process of a cold front moving southward, emphasizing the vertical structure of the boundary layer and the influence on the three-dimensional structure of PM2.5. Our observations revealed that the heavy pollution near the surface was located at sparse or equal isobar in front of the cold front. During the process of the weather system moving in, the timing of the maximum PM2.5 concentration at each station from north to south was delayed. The results show that the WRF-Chem model can better capture the spatial and temporal variations of surface and upper air meteorological elements and PM2.5 concentrations over eastern China. The simulation results show that the boundary layer structure and the vertical profile of PM2.5 at the same location of the mobile weather system showed similar characteristics. When the invasion takes place in a cold front, the pollutants in front of the cold front are rapidly lifted from the ground to a high altitude. The growth in PM2.5 concentration and the increasing wind speed at high altitudes lead to the upward trend in PM2.5 flux. As the altitude increases, the high-value area of PM2.5 concentration tilts towards the warm air mass. The transit of the cold front at night led to more unstable convection within the boundary layer; the height of the boundary layer increased from north to south, reaching over 1 km, breaking the rules characteristic of the diurnal evolution of the boundary layer. The results indicate that the combination of vertical observations and elaborate simulation can effectively explain the impact of synoptic processes on the transport, distribution, and evolution of air pollution and provide precise pollution-control directives.

Modeling study of aerosol-meteorology feedback during winter haze events over the north China plain

An online-coupled regional atmospheric chemistry/aerosol-climate model RIEMS-Chem was utilized to investigate the aerosol radiative feedback effect during winter hazes over the North China Plain. Three severe winter haze events (8–January 15, 2013, 20–February 26, 2014, and 15–December 22, 2016) were studied. Model comparison against observations showed a reduction of absolute model bias by 16–21% for PM2.5 when aerosol radiative effect (ARE) was included. Due to ARE, the period- and regional-mean changes in 2 m air temperature (T2), planetary boundary layer height (PBLH), and PM2.5 concentration were −0.4∼-1.8 °C (−14%∼-60%), −30.9∼-183.6 m (−12%∼-31%), and 37.7–61.1 μg/m3 (26%–47%), respectively. The magnitudes of changes in downward solar radiation (SWD), T2, and PBLH were proportional to AOD (aerosol optical depth) and PM2.5 column level but not to surface PM2.5 level. The response of PM2.5 to aerosol radiative feedback was dependent on both PM2.5 level and ARE. The average Integrated Process Rate (IPR) for PM2.5 in Beijing was estimated to be 11.9 μg/m3/hr during the growth stage of the three severe hazes. Aerosol radiative feedback caused a net increase of IPR for PM2.5 by 2.1–9.5 μg/m3/hr during the growth stages, mostly contributed by chemical processes. Linear relationship can be derived for the PM2.5-ΔPM2.5 data pairs over the study region. The high correlation coefficient (0.95–0.96) suggested PM2.5 could be an indicator of the feedback-induced ΔPM2.5. A threshold PM2.5 concentration of 75 μg/m3 was found above which more than 10% PM2.5 change would occur due to aerosol radiative feedback, but this value differed among haze events and cities.

Determination of mixing layer height from co-located lidar, ceilometer and wind Doppler lidar measurements: Intercomparison and implications for PM2.5 simulations

Mixing layer height (MLH) is retrieved at an urban site in Seoul, Korea, from 2-year collocated remote sensing instrument measurements: elastic aerosol lidar, ceilometer, and wind Doppler lidar (WDL). All three measurements captured clear diurnal patterns of MLH development and break down, but large discrepancies were noted in the nocturnal MLH and the onset time of MLH development. Vertical wind speed standard deviation (σw) from WDL measurements displayed low MLHs during nighttime, compared to MLH retrieved from aerosol backscattering intensity profiles (mean bias = −0.21 km). To this extent, mixing represented by σw showed close correlation with surface heating but significant discrepancies with the vertical distribution of atmospheric constituents, especially at night. Comparison of MLH from elastic aerosol lidar and WDL with WRF-Chem simulation results showed that WRF-Chem MLH closely resembled MLH retrieved from WDL, indicating that WRF-Chem sufficiently simulated turbulence, but displayed lower nocturnal MLH compared to lidar-derived MLH. Furthermore, investigation of the vertical profiles of PM2.5 in the model displayed confined distribution of aerosols within the nocturnal MLH, leading to overestimation of surface PM2.5 concentration. For the case studies of May 12 and 18, 2016, implicating full mixing within the MLH defined by aerosol lidar measurements on WRF-Chem PM2.5 vertical distribution simulations was shown to significantly reduce WRF-Chem PM2.5 simulation errors from mean normalized bias of 52% to 19%.

Investigation of Coastal Atmospheric Boundary Layer and Particle by Unmanned Aerial Vehicle under Different Land-sea Temperature

During summer daytime, the surface PM2.5 concentration tends to rise briefly in the west coast of Bohai Sea, which might be related to the thermal internal boundary layer (TIBL) formed by the land-sea thermal interaction. In this study, we investigated the relationship between land-sea air temperature and pollutant concentration in summer over a coastal region of northern China using an unmanned aerial vehicle (UAV)-measurement platform to obtain vertical meteorological data and pollutant concentration. In midday, when only considered onshore winds, PM2.5 concentration was significantly higher than that in not considered onshore winds, meanwhile the high positive correlation between land-sea temperature difference and particle concentration was identified. It can be seen from the UAV observed profiles that the TIBLs were formed at the bottom of the atmospheric layer in the daytime with height values in the range of 44–97 m, resulting from the impact of onshore winds caused by land-sea thermal difference. This land-sea thermal difference influences the atmospheric boundary layer (ABL) and TIBL structures, also the PM2.5 concentration diffusion. We found that larger land-sea temperature difference could induce lower coastal ABL height and larger potential temperature vertical gradients below TIBL, even delaying the stable layer establishment. When TIBL and ABL heights increased, as a result, the height of the maximum PM2.5 concentration also increased. In addition, TIBL could lead to an increase in surface PM2.5 concentration lasting for 2 hours during the daytime. Our results establish the relationship between land-sea temperature difference and particle diffusion, and have an important role in coastal air quality forecast.

Seasonal patterns in rice and wheat residue burning and surface PM2.5 concentration in northern India

Rice-Wheat production system of the Indo-Gangetic Plain (IGP) in northern India generates large amounts of crop residues annually, substantial amount of which is burned in-situ. Due to this, people in this region and nearby cities are exposed to extremely high concentrations of atmospheric pollutants, especially in late autumn to winter. Multiple factors affect air quality, the main ones being pollutant emissions and meteorology. We generated a high-resolution (0.05° × 0.05°) multi-year (2012–2020) fine particular matter (PM 2.5 ) dataset of pollutant emissions from wheat and rice residue burning for the IGP by combining production data and satellite active fires from the Visible Infrared Imaging Radiometer Suite (VIIRS) 375-m product. We used surface PM 2.5 concentrations and meteorological data from Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) atmospheric reanalysis to characterize the seasonal PM 2.5 concentrations and their relationships with anomalies in meteorological conditions. Results showed a much higher number of fires during from rice, with an interannual average of 79,385 total fires versus 33,096 from wheat. Emissions are higher during the rice harvest period: 406 Gg/year versus 245 Gg/year, respectively for rice and wheat. Emissions and PM 2.5 concentrations from rice harvest are higher than those of wheat as a consequence of a higher number of fire spots, emissions, and atmospheric conditions that prevent their dispersion. PM 2.5 concentrations are in addition strongly related to the amount of biomass burned and the number of fires and their intensity. In terms of meteorological anomalies, the planetary boundary layer height, wind speed and the surface thermal inversion strength have a stronger association with PM 2.5 concentration during the rice residue burning period. Conversely, PM 2.5 concentration and emissions anomalies during the wheat harvest shows a stronger correlation with factors associated with the amount of residues burned in the field. • Fire spots and PM 2.5 emissions from both rice and wheat residue burning concentrate over the west area of north India. • Maximum PM 2.5 emissions from rice (post-monsoon) residue burning are about twice those from wheat (pre-monsoon). • Post-monsoon PM 2.5 concentrations from rice residue burning are about three times higher than from wheat residue burning (pre-monsoon). • PM 2.5 shows a greater association with inversion strength and relative humidity during the rice harvest period.

Analysis of Air Pollution Data in India between 2015 and 2019

India suffers from among the worst air pollution in the world. In response, a large government effort to increase air quality monitoring is underway. We present the first comprehensive analysis of government air quality observations from 2015–2019 for PM10, PM2.5, SO2, NO2 and O3 from the Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring (CAAQM) network and the manual National Air Quality Monitoring Program (NAMP), as well as PM2.5 from the US Air-Now network. We address inconsistencies and data gaps in datasets using a rigorous procedure to ensure data representativeness. We find particulate pollution dominates the pollution mix across India with virtually all sites in northern India (divided at 23.5°N) exceeding the annual average PM10 and PM2.5 residential national ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India exceeding the PM10 standard by 50% and the PM2.5 standard by 40%. Annual average SO2, NO2 and MDA8 O3 generally meet the residential NAAQS across India. Northern India has (~10%–130%) higher concentrations of all pollutants than southern India, with only SO2 having similar concentrations. Although inter-annual variability exists, we found no significant trend of these pollutants over the five-year period. In the five cities with Air-Now PM2.5 measurements - Delhi, Kolkata, Mumbai, Hyderabad and Chennai, there is reasonable agreement with CPCB data. The PM2.5 CPCB CAAQM data compares well with satellite derived annual surface PM2.5 concentrations (Hammer et al., 2020), with the exception of the western desert region prior to 2018 when surface measurements exceeded satellite retrievals. Our reanalyzed dataset is useful for evaluation of Indian air quality from satellite data, atmospheric models, and low-cost sensors. Our dataset also provides a baseline to evaluate the future success of National Clean Air Programme as well as aids in assessment of existing and future air pollution mitigation policies.

Relationships between 3D urban form and ground-level fine particulate matter at street block level: Evidence from fifteen metropolises in China

Substantial efforts have been devoted to exploring the effects of urban form on fine particulate matter (PM2.5), but the complexity has been far away from being fully understood. The current remarkable inconsistencies with regards to measurements, ascertainment, and findings make the evidence across continents, regions, or cities be necessary to verify the robustness and generalizability of urban form effects. Besides, existing measurements of urban form are often unsystematic and limit analyses in both horizontal and vertical dimensions. In this paper, fifteen metropolises in China were selected to examine the relationships between three-dimensional (3D) urban form and PM2.5 concentrations at the street block level, using 3D spatial metrics and multivariate linear regression. Satellite-derived surface PM2.5 estimates of fine spatial resolution, building footprint, and multiple geographic open datasets were used. Our results demonstrated that urban form effects hold for the metropolises in China, and street accessibility, length of road segments, topography, urban vegetation, surrounding open and green spaces, and transportation facilities were found to be the influential factors of the concentrations of PM2.5. We also revealed the complicated and place-varying effects of urban form indicators, represented by the largely different or opposite effects of many urban form indicators in different cities. For example, building density, building height, and land use mixture have relatively limited and inconsistent effects in most cities. Results of this work suggest critical reflections on some of the current ideas that have been accepted to be vital for improving PM2.5.

Improving surface PM2.5 forecasts in the United States using an ensemble of chemical transport model outputs: 2. bias correction with satellite data for rural areas.

This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi-satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multi chemical transport model (CTM) (GEOS-Chem, WRF-Chem and CMAQ) outputs and ground observations. In part I of the study, a multi-model ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non-rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125 - 300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non-rural areas, referred to as "extended ground truth" or EGT, for today. Lastly, we applied the KF technique to update the bias in the forecast for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three models for both today and next day show the best performance. Together, the two-part study develops a multi-model and multi-AOD bias correction technique that has the potential to improve PM2.5 forecasts in both rural and non-rural areas in near real time, and be readily implemented at state levels.

Impacts of uncertainties in emissions on aerosol data assimilation and short-term PM2.5 predictions over Northeast Asia

To improve PM2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations. In contrast to the conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every 6 h during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-h PM2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM2.5 predictions in Northeast Asia.

Spatiotemporal variations of surface ozone and its influencing factors across Tibet: A Geodetector-based study

Reasons regarding surface ozone formation and distribution in remote regions is limited. Tibet is an important remote area on Earth, with various climates and extremely high elevation (average ~ 4000 m), which makes it a good place to study the spatiotemporal distribution of surface ozone and explore the causes. Based on ground monitoring data from 18 stations on Tibet between 2015 and 2019, the annual, seasonal, monthly, and diurnal variations of surface ozone were analyzed. The annual mean values (60.7–72.5 μg/m3) presented an increasing trend during the past five years, with seasonal concentrations of surface ozone higher in spring than in winter. Spatially, both the ground observations and high-resolution remote sensing data indicated that the surface ozone was relatively high in the southwest regions of Tibet, and low in the southeast and northeast areas. Geodetector analysis found that relative humidity (RH), normalized difference vegetation index (NDVI), and solar radiation (SR) were the top three individual factors affecting surface ozone distribution, while NO2, PM10, and PM2.5 showed less influence. All influencing factors showed an improvement through the two-factor interaction. The associations of RH∩PM10 (q = 0.77), RH∩NDVI (q = 0.72), and NDVI∩SR (q = 0.73) exhibited a strong impact on surface ozone distribution, suggesting that places with sparse vegetation cover, dry climate and strong SR would usually cause high atmospheric ozone burden. This could also explain why concentrations of surface ozone continue to increase in some remote areas worldwide with ecological deterioration and desertification.

Using Lidar and Historical Similar Meteorological Fields to Evaluate the Impact of Anthropogenic Control on Dust Weather During COVID-19

Asian dust can be transported at least one full circuit around the globe. During the transportation, dust can interact with local air-borne dust and pollutants, and has a profound impact on the environment. A novel coronavirus (COVID-19) has been affecting human activities worldwide since early 2020. The Chinese government has implemented emergency control measures. Since April 2020, control measures to reduce anthropogenic emissions have been gradually reduced. The optical properties of aerosols during the dust transport were affected by meteorological conditions, local environmental conditions and human activities. Therefore, two dust weather processes in March 2018 and March 2020 were screened under similar meteorological fields and transportation paths, which were mainly affected by human activities. Based on lidar data, in East China, compared with 2018, the average aerosol optical depth (AOD) of all types of aerosols at 0–4 km in 2020 decreased by 55.48%, while the AOD of dust aerosols decreased by 43.59%. The average particle depolarization ratio and color ratio decreased by 40.33 and 10.56% respectively. Due to the reduction of anthropogenic emissions in China (detected by lidar), the concentration of surface PM2.5 decreased by 57.47%. This indicated that due to the decrease in human activities caused by COVID-19 control measures, the optical properties of aerosols were significantly reduced during dust weather process in eastern China. However, in the Pacific region, compared with 2018, the AOD values of 0–1 km layer and 1–6 km layer in 2020 increased by 56.4% and decreased by 29.2% respectively. The difference between the two contributions of dust aerosols was very small. Meanwhile, compared with 2018, China’s near surface pollutants decreased significantly in 2020, indicating that the near surface AOD of the Pacific in 2020 was mainly contributed by local pollutants. This study was of great significance to the study of long-range and cross regional transport of pollutants.

Changes in the Distribution Pattern of PM2.5 Pollution over Central China

The extent of PM2.5 pollution has reduced in traditional polluted regions such as the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin (SB), and Pearl River Delta (PRD) over China in recent years. Despite this, the Twain-Hu Basin (THB), which covers the lower flatlands in Hubei and Hunan provinces in central China, was found to be a high PM2.5 pollution region, with annual mean PM2.5 concentrations of 41–63 μg·m−3, which is larger than the values in YRD, SB, and PRD during 2014–2019, and high aerosol optical depth values (>0.8) averaged over 2000–2019 from the MODIS products. Heavy pollution events (HPEs) are frequently observed in the THB, with HPE-averaged concentrations of PM2.5 reaching up to 183–191 μg·m−3, which exceeds their counterparts in YRD, SB, and PRD for 2014–2019, highlighting the THB as a center of heavy PM2.5 pollution in central China. During 2014–2019, approximately 65.2% of the total regional HPEs over the THB were triggered by the regional transport of PM2.5 over Central and Eastern China (CEC). This occurred in view of the co-existing HPEs in the NCP and the THB, with a lag of almost two days in the THB-PM2.5 peak, which is governed by the strong northerlies of the East Asian monsoon (EAM) over CEC. Such PM2.5 transport from upstream source regions in CEC contributes 60.3% of the surface PM2.5 pollution over the THB receptor region. Hence, a key PM2.5 receptor of the THB in regional pollutant transport alters the distribution patterns of PM2.5 pollution over China, which is attributable to the climate change of EAMs. This study indicates a complex relationship between sources and receptors of atmospheric aerosols for air quality applications.

Contrasting Trends of Surface PM2.5, O3, and NO2 and Their Relationships with Meteorological Parameters in Typical Coastal and Inland Cities in the Yangtze River Delta.

The contrasting trends of surface particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city of Hefei (HF), located in the Yangtze River Delta (YRD). In both cities, PM2.5 declined substantially, while O3 and NO2 showed peak values during 2017 when the most frequent extreme high-temperature events occurred. Wind speed was correlated most negatively with PM2.5 and NO2 concentrations, while surface temperature and relative humidity were most closely related to O3. All of the studied pollutants were reduced by rainfall scavenging, with the greatest reduction seen in PM2.5, followed by NO2 and O3. By contrast, air pollutants in the two cities were moderately strongly correlated, although PM2.5 concentrations were much lower and Ox (O3 + NO2) concentrations were higher in SH. Additionally, complex air pollution hours occurred more frequently in SH. Air pollutant concentrations changed more with wind direction in SH. A more effective washout effect was observed in HF, likely due to the more frequent strong convection and thunderstorms in inland areas. This research suggests pertinent air quality control measures should be designed accordingly for specific geographical locations.

Variations in Nocturnal Residual Layer Height and Its Effects on Surface PM2.5 over Wuhan, China

Large amounts of aerosols remain in the residual layer (RL) after sunset, which may be the source of the next day’s pollutants. However, the characteristics of the nocturnal residual layer height (RLH) and its effect on urban environment pollution are unknown. In this study, the characteristics of the RLH and its effect on fine particles with diameters <2.5 μm (PM2.5) were investigated using lidar data from January 2017 to December 2019. The results show that the RLH is highest in summer (1.55 ± 0.55 km), followed by spring (1.40 ± 0.58 km) and autumn (1.26 ± 0.47 km), and is lowest in winter (1.11 ± 0.44 km). The effect of surface meteorological factors on the RLH were also studied. The correlation coefficients (R) between the RLH and the temperature, relative humidity, wind speed, and pressure were 0.38, −0.18, 0.15, and −0.36, respectively. The results indicate that the surface meteorological parameters exhibit a slight correlation with the RLH, but the high relative humidity was accompanied by a low RLH and high PM2.5 concentrations. Finally, the influence of the RLH on PM2.5 was discussed under different aerosol-loading periods. The aerosol optical depth (AOD) was employed to represent the total amount of pollutants. The results show that the RLH has an effect on PM2.5 when the AOD is small but has almost no effect on PM2.5 when the AOD is high. In addition, the R between the nighttime mean RLH and the following daytime PM2.5 at low AOD is −0.49, suggesting that the RLH may affect the following daytime surface PM2.5. The results of this study have a guiding significance for understanding the interaction between aerosols and the boundary layer.

Relating geostationary satellite measurements of aerosol optical depth (AOD) over East Asia to fine particulate matter (PM<sub>2.5</sub>): insights from the KORUS-AQ aircraft campaign and GEOS-Chem model simulations

Abstract. Geostationary satellite measurements of aerosol optical depth (AOD) over East Asia from the Geostationary Ocean Color Imager (GOCI) and Advanced Himawari Imager (AHI) instruments can augment surface monitoring of fine particulate matter (PM2.5) air quality, but this requires better understanding of the AOD–PM2.5 relationship. Here we use the GEOS-Chem chemical transport model to analyze the critical variables determining the AOD–PM2.5 relationship over East Asia by simulation of observations from satellite, aircraft, and ground-based datasets. This includes the detailed vertical aerosol profiling over South Korea from the KORUS-AQ aircraft campaign (May–June 2016) with concurrent ground-based PM2.5 composition, PM10, and AERONET AOD measurements. The KORUS-AQ data show that 550 nm AOD is mainly contributed by sulfate–nitrate–ammonium (SNA) and organic aerosols in the planetary boundary layer (PBL), despite large dust concentrations in the free troposphere, reflecting the optically effective size and high hygroscopicity of the PBL aerosols. We updated SNA and organic aerosol size distributions in GEOS-Chem to represent aerosol optical properties over East Asia by using in situ measurements of particle size distributions from KORUS-AQ. We find that SNA and organic aerosols over East Asia have larger size (number median radius of 0.11 µm with geometric standard deviation of 1.4) and 20 % larger mass extinction efficiency as compared to aerosols over North America (default setting in GEOS-Chem). Although GEOS-Chem is successful in reproducing the KORUS-AQ vertical profiles of aerosol mass, its ability to link AOD to PM2.5 is limited by under-accounting of coarse PM and by a large overestimate of nighttime PM2.5 nitrate. The GOCI–AHI AOD data over East Asia in different seasons show agreement with AERONET AODs and a spatial distribution consistent with surface PM2.5 network data. The AOD observations over North China show a summer maximum and winter minimum, opposite in phase to surface PM2.5. This is due to low PBL depths compounded by high residential coal emissions in winter and high relative humidity (RH) in summer. Seasonality of AOD and PM2.5 over South Korea is much weaker, reflecting weaker variation in PBL depth and lack of residential coal emissions.

Examining the competing effects of contemporary land management vs. land cover changes on global air quality

Abstract. Our work explores the impact of two important dimensions of land system changes, land use and land cover change (LULCC) as well as direct agricultural reactive nitrogen (Nr) emissions from soils, on ozone (O3) and fine particulate matter (PM2.5) in terms of air quality over contemporary (1992 to 2014) timescales. We account for LULCC and agricultural Nr emissions changes with consistent remote sensing products and new global emission inventories respectively estimating their impacts on global surface O3 and PM2.5 concentrations as well as Nr deposition using the GEOS-Chem global chemical transport model. Over this time period, our model results show that agricultural Nr emission changes cause a reduction of annual mean PM2.5 levels over Europe and northern Asia (up to −2.1 µg m−3) while increasing PM2.5 levels in India, China and the eastern US (up to +3.5 µg m−3). Land cover changes induce small reductions in PM2.5 (up to −0.7 µg m−3) over Amazonia, China and India due to reduced biogenic volatile organic compound (BVOC) emissions and enhanced deposition of aerosol precursor gases (e.g., NO2, SO2). Agricultural Nr emission changes only lead to minor changes (up to ±0.6 ppbv) in annual mean surface O3 levels, mainly over China, India and Myanmar. Meanwhile, our model result suggests a stronger impact of LULCC on surface O3 over the time period across South America; the combination of changes in dry deposition and isoprene emissions results in −0.8 to +1.2 ppbv surface ozone changes. The enhancement of dry deposition reduces the surface ozone level (up to −1 ppbv) over southern China, the eastern US and central Africa. The enhancement of soil NO emission due to crop expansion also contributes to surface ozone changes (up to +0.6 ppbv) over sub-Saharan Africa. In certain regions, the combined effects of LULCC and agricultural Nr emission changes on O3 and PM2.5 air quality can be comparable (>20 %) to anthropogenic emission changes over the same time period. Finally, we calculate that the increase in global agricultural Nr emissions leads to a net increase in global land area (+3.67×106km2) that potentially faces exceedance of the critical Nr load (>5 kg N ha−1 yr−1). Our result demonstrates the impacts of contemporary LULCC and agricultural Nr emission changes on PM2.5 and O3 in terms of air quality, as well as the importance of land system changes for air quality over multidecadal timescales.