Ground Particulate Matter Research Articles

Reconstructing long-term (1980–2022) daily ground particulate matter concentrations in India (LongPMInd)

Abstract. Severe airborne particulate matter (PM, including PM2.5 and PM10) pollution in India has caused widespread concern. Accurate PM concentrations are fundamental for scientific policymaking and health impact assessment, while surface observations in India are limited due to scarce sites and uneven distribution. In this work, a simple structured, efficient, and robust model based on the Light Gradient-Boosting Machine (LightGBM) was developed to fuse multisource data and estimate long-term (1980–2022) historical daily ground PM concentrations in India (LongPMInd). The LightGBM model shows good accuracy with out-of-sample, out-of-site, and out-of-year cross-validation (CV) test R2 values of 0.77, 0.70, and 0.66, respectively. Small performance gaps between PM2.5 training and testing (delta RMSE of 1.06, 3.83, and 7.74 µg m−3) indicate low overfitting risks. With great generalization ability, the openly accessible, long-term, and high-quality daily PM2.5 and PM10 products were then reconstructed (10 km, 1980–2022). This showed that India has experienced severe PM pollution in the Indo-Gangetic Plain (IGP), especially in winter. PM concentrations have significantly increased (p<0.05) in most regions since 2000 (0.34 µgm-3yr-1). The turning point occurred in 2018 when the Indian government launched the National Clean Air Programme, and PM2.5 concentrations declined in most regions (−0.78 µgm-3yr-1) during 2018–2022. Severe PM2.5 pollution caused continuous increased attributable premature mortalities, from 0.73 (95 % confidence interval (CI) [0.65, 0.80]) million in 2000 to 1.22 (95 % CI [1.03, 1.41]) million in 2019, particularly in the IGP, where attributable mortality increased from 0.36 million to 0.60 million. LongPMInd has the potential to support multiple applications of air quality management, public health initiatives, and efforts to address climate change. The daily and monthly PM2.5 and PM10 concentrations are publicly accessible at https://doi.org/10.5281/zenodo.10073944 (Wang et al., 2023a).

Estimating particulate matter concentrations and meteorological contributions in China during 2000–2020

Estimating the effects of airborne particulate matter (PM) on climate and human health is highly dependent on the accurate prediction of its concentration and size distribution. High-complexity machine learning models have been widely used for PM concentration prediction, but such models are often considered as “black boxes”, lacking interpretability. Here, a simple structure lightGBM model is built for ground PM estimation, and the SHAP approach is used to separate the meteorological contributions due to its strong influence on PM concentration. The models show good performance with correlation coefficient (R2) of 0.84–0.88, 0.80–0.85, and 0.71–0.79, for PM2.5, PM10, and PM2.5-10 (2.5–10 μm), respectively. The lightGBM model trains 45 times faster than the XGBoost model while showing similar accuracy. More importantly, the models have small performance gaps between training and predicting (delta R2: 0.07–0.12), effectively reducing overfitting risk. The PM datasets (10 km daily) of three size ranges are then generated over China from 2000 to 2020. The SHAP method shows good agreement with the meteorological normalization approach in separating the meteorological contributions (R2 > 0.5). In the Beijing-Tianjin-Hebei region (BTH), meteorology has greater influence on PM2.5-10 (−5.66%–9.99%) than PM2.5 and PM10. In the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), albedo has a large contribution to PM2.5 concentration under the influence of solar radiation. Notably, relative humidity (RH) has different seasonal effects on PM of three size ranges. In the BTH region, RH has negative effects on PM2.5 (−0.52 μg/m3) and positive effects on PM10 (1.01 μg/m3) and PM2.5-10 (3.39 μg/m3) in spring, but has opposite effects in summer. The results of SHAP approach are consistent with existing conclusions and imply its feasibility in explaining haze formation. The generated PM datasets are useful in health assessment, environmental management, and climate change studies.

Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data.

Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM10) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014-2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March-April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM10 concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM10 than Terra AODs-PM10 with correlation coefficients (r) of 0.72-0.83 and 0.57-0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (r = 0.65-0.73) than with CO total columns (r = 0.56-0.72). The r values of satellite and ground datasets were greatest when the analysis was restricted to November-March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM10 and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited.

Spatial Particulate Fields during High Winds in the Imperial Valley, California

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.

Satellite AOD conversion into ground PM10, PM2.5 and PM1 over the Po valley (Milan, Italy) exploiting information on aerosol vertical profiles, chemistry, hygroscopicity and meteorology

Satellite data are fundamental for air quality monitoring. Despite this, several algorithms are present in literature leaving to the reader a choice on the best approach without considering the physical – spatial – temporal inconsistencies concerning the problem of relating satellite with ground-based data. In order to overcome this dilemma, the present work was carried out developing a general methodological approach to overcome the aforementioned inconsistencies obtaining in any region the best retrieval. In this respect, the particulate matter phenomenology (both at ground and vertically) was considered in order to select the best approach for the AOD to PM conversion starting from the knowledge of PM properties, meteorology and vertical behaviour.In this respect, multiple years of particulate matter vertical profiles, chemical composition, hygroscopicity, meteorology and optical properties were used to define an optimized satellite AOD-ground PM relationship. A hybrid algorithm (physically based and statistical) was developed to be applied over the Milan conurbation (Po Valley) to determine ground-level PM1, PM2.5 and PM10 concentrations from satellite aerosol optical depth (AOD) data. The developed algorithm (based on aerosol optical depth, mixing height, wind speed and PM concentrations of the previous day) showed high accuracy enabling to predict ground particulate matter concentrations with very low RMSE in prediction (5.48 μg/m3, 9.89 μg/m3 and 10.64 μg/m3 for PM1, PM2.5 and PM10) and rather high R2 (≥0.83 on the evaluation set of data).

An inclusive view of Saharan dust advections to Italy and the Central Mediterranean

We address observations of physical and chemical properties of Saharan dust advections (SDA) as observed in the Central Mediterranean basin, within the framework of the LIFE+10, DIAPASON project (www.diapason-life.eu). DIAPASON aimed at the definition of best practices and tools to detect and evaluate the contribution of Saharan dust to ground particulate matter (PM) loads. Polarization-sensitive, automated lidar-ceilometers (PLC) are one of the tools prototyped and used in the Rome area to reach this goal. The results presented in this study focus on: 1) the effectiveness of various observational tools at detecting and characterizing atmospheric dust plumes, and 2) processes and properties of Saharan dust advections reaching the central Mediterranean region. In this respect, the combination of numerical model forecasts and time-resolved (at least hourly) PLC or chemical observations was found to constitute an efficient way to predict and confirm the presence of Saharan dust. In the period 2011–2014, Saharan dust advections were observed to reach over Rome on about 32% of the days. In some 70% of these days the dust reached the ground in dry conditions, while 30% of advection days involved wet deposition. Dry (wet) deposition was found to maximize (minimize) in summer. The northern Sahara between Algeria and Tunisia (Grand Erg Oriental), was confirmed as the most frequent region of origin of the dust mobilized towards central Italy. Secondary source regions include northern Morocco and Libya. On a statistical basis, Saharan advections to Rome were preceded by increasing atmospheric pressure and stability. These conditions were found to favor the accumulation of aerosols related to local emission sources before the SDA reached the ground. Meteorology (precipitation and turbulence in primis) resulted to be an important modulator of PM concentrations during SDAs. Magnitude and timing of these factors should be well considered to correctly evaluate the dust share in PM loads or the related health effects. Saharan advections observed during DIAPASON affected particle concentrations down to diameters of about 0.6–1 μm, with number concentrations peaking at the 2.5 μm diameter range. These advections were associated with a significant increase in Si-rich particles containing a non-negligible fraction of water. Rainfall was observed to preferentially remove dust particles larger than 2 μm, causing a significant depletion in the Ca-rich fraction with respect to the Si-rich one. The increase in PLC depolarization ratios above 5%, as well as the hourly PIXE records of the Si/Ca ratio increasing above 1 were found to represent good markers for the actual presence of Saharan dust particulate matter, when Saharan advection conditions are occurring.

Appraising the effects of atmospheric aerosols and ground particulates concentrations on GPS-derived PWV estimates

In 2016, three Nigerian cities were listed amongst the World's most polluted in terms of particulate matter (PM). Acknowledging Nigeria's limited resources for outdoor air pollution monitoring, this study attempts to investigate the effects on atmospheric aerosol optical depth and ground PM on GPS derived-precipitable water vapour estimates. The study utilized available GPS-derived precipitable water vapour (GPSPWV), the moderate resolution imaging spectroradiometer aerosol optical depth (MODISAOD) and the ground level particulate matter of less than 10 μm (GPM) datasets for December 2015–November 2016. All the datasets were tested for normality. To evaluate the atmospheric aerosol properties, the MODISPWV estimates were pre-validated using the GPSPWV measurements. The results revealed GPSPWV-MODISPWV agreement (R = 0.964; RMSE = 3.810 mm). The GPSPWV-MODISAOD analysis showed relationship (R ≤ −0.636; RMSE ≤ 0.563) for the atmospheric aerosol experiment, while the collocating GPSPWV-GPM seasonal analysis also revealed significant correlation (R < −0.660). The correlation of combined seasonal datasets for the GPSPWV-MODISAOD and GPSPWV-GPM relationships showed high negative correlation values of 0.79 and 0.68 respectively. The findings of this study is in agreement with similar related studies, as well as serve as position accuracy for future related studies.

A Bayesian Downscaler Model to Estimate Daily PM2.5 Levels in the Conterminous US.

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM2.5 with an R2 at 70% and generated reliable annual and seasonal PM2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM2.5 exposure assessments and can also quantify the prediction errors.

New insights into the vertical structure of the September 2015 dust storm employing eight ceilometers and auxiliary measurements over Israel

Abstract. On 7 September 2015, an unprecedented and unexceptional extreme dust storm struck the eastern Mediterranean (EM) basin. Here, we provide an overview of the previous studies and describe the dust plume evolution over a relatively small area, i.e., Israel. This study presents vertical profiles provided by an array of eight ceilometers covering the Israeli shore, inland and mountain regions. We employ multiple tools including spectral radiometers (Aerosol Robotic Network – AERONET), ground particulate matter concentrations, satellite images, global/diffuse/direct solar radiation measurements and radiosonde profiles. The main findings reveal that the dust plume penetrated Israel on 7 September from the northeast in a downward motion to southwest. On 8 September, the lower level of the dust plume reached 200 m above ground level, generating aerosol optical depth (AOD) above 3 and extreme ground particulate matter concentrations up to ∼ 10 000 µm m−3. A most interesting feature on 8 September was the very high variability in the surface solar radiation in the range of 200–600 W m−2 (22 sites) over just a distance of several hundred kilometers in spite of the thick dust layer above. Furthermore, 8 September shows the lowest radiation levels for this event. On the following day, the surface solar radiation increased, thus enabling a late (between 11:00 and 12:00 UTC) sea breeze development mainly in the coastal zone associated with a creation of a narrow dust layer detached from the ground. On 10 September, the AOD values started to drop down to ∼ 1.5, and the surface concentrations of particulate matter decreased as well as the ceilometers' aerosol indications (signal counts) although Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) revealed an upper dust layer remained.

Monthly analysis of PM ratio characteristics and its relation to AOD

ABSTRACTAirborne particulate matter (PM) is derived from diverse sources—natural and anthropogenic. Climate change processes and remote sensing measurements are affected by the PM properties, which are often lumped into homogeneous size fractions that show spatiotemporal variation. Since different sources are attributed to different geographic locations and show specific spatial and temporal PM patterns, we explored the spatiotemporal characteristics of the PM2.5/PM10 ratio in different areas. Furthermore, we examined the statistical relationships between AERONET aerosol optical depth (AOD) products, satellite-based AOD, and the PM ratio, as well as the specific PM size fractions. PM data from the northeastern United States, from San Joaquin Valley, CA, and from Italy, Israel, and France were analyzed, as well as the spatial and temporal co-measured AOD products obtained from the MultiAngle Implementation of Atmospheric Correction (MAIAC) algorithm. Our results suggest that when both the AERONET AOD and the AERONET fine-mode AOD are available, the AERONET AOD ratio can be a fair proxy for the ground PM ratio. Therefore, we recommend incorporating the fine-mode AERONET AOD in the calibration of MAIAC. Along with a relatively large variation in the observed PM ratio (especially in the northeastern United States), this shows the need to revisit MAIAC assumptions on aerosol microphysical properties, and perhaps their seasonal variability, which are used to generate the look-up tables and conduct aerosol retrievals. Our results call for further scrutiny of satellite-borne AOD, in particular its errors, limitations, and relation to the vertical aerosol profile and the particle size, shape, and composition distribution. This work is one step of the required analyses to gain better understanding of what the satellite-based AOD represents.Implications: The analysis results recommend incorporating the fine-mode AERONET AOD in MAIAC calibration. Specifically, they indicate the need to revisit MAIAC regional aerosol microphysical model assumptions used to generate look-up tables (LUTs) and conduct retrievals. Furthermore, relatively large variations in measured PM ratio shows that adding seasonality in aerosol microphysics used in LUTs, which is currently static, could also help improve accuracy of MAIAC retrievals. These results call for further scrutiny of satellite-borne AOD for better understanding of its limitations and relation to the vertical aerosol profile and particle size, shape, and composition.

On the AOD contribution to ground PM prediction models

Introduction. The use of satellite observations for estimating ground level particulate matter (PM) concentrations is growing. Specifically, numerous researchers use satellite-based Aerosol Optical Depth (AOD) as an explanatory variable in different statistical models for estimating PM concentrations. Whereas some models use only the AOD, others use additional variables, such as meteorological, environmental, and land use variables. AOD measures light extinction at a given wavelength due to scattering and absorption along the measured atmospheric column. The rationale behind using the AOD stems from the physical interaction of airborne PM with electromagnetic radiation, and since it has a much better spatial coverage than ground PM measurements. In many works AOD is the primary variable in spite of the coarse temporal resolution of the MODIS-based AOD (once per day) and the large number of missing data due to clouds. Methods. This work assesses the actual contribution of AOD to PM estimations by analyzing (a) a large database of fine and coarse PM concentrations from different geographic locations, (b) satellite-borne AOD obtained with different spatial grid by applying different algorithms on MODIS raw data, and (c) applying various statistical models to link these databases. Results. We found that the date, as a variable, has a crucial effect on the performance of all the models, including mixed effects models (MEM) that consistently result in better predictions. In fact, the seemingly higher explained spatiotemporal variability of MEMs results from the model fitting the temporal PM patterns, for which the date is a strong predictor. Surprisingly, the overall AOD contribution to the explained variability is little. Conclusion. While AOD has a lot of potential for PM estimation in locations where monitoring is very sparse or not existing at all, currently its actual contribution to predicted PM levels is marginal, and should be further studied.

Relationship of MISR component AODs with black carbon and other ground monitored particulate matter composition

This study assessed the relationship between the satellite Aerosol Optical Depths (AODs) and the ground monitored concentrations of particulate matter (PM) mass and its major constituents (black carbon–BC, organic carbon–OC, sulfates and nitrates), respectively. Both component AOD and total AOD products of Multi–angel Imaging Spectro Radiometer (MISR) were used for comparison along with the AOD product of the Moderate Resolution Imaging Spectroradiometer (MODIS). The ground PM data available during the period from 2004 to 2010 at the Asian Institute of Technology (AIT), a suburb site of the Bangkok Metropolitan Region, was used. MODIS and MISR AODs were validated against Sun photometer AOD, monitored at the Pimai AERONET station which showed strong linear regression with high R2 values of 0.87 and 0.92, respectively. The correlation coefficients between MODIS and MISR AODs and PM mass concentrations, respectively, were improved after exclusion of observations with cloud cover above 3/10. The R values (square root of determination coefficient R2) for linear relationships between PM10 and MODIS AOD were accordingly increased from 0.33 to 0.58 for MODIS AOD and from 0.25 to 0.54 for MISR AOD, while those for PM2.5 were improved from 0.30 to 0.55 for MODIS AOD and from 0.31 to 0.43 for MISR AOD. The stepwise regression was conducted to analyze the relationship between MISR component AODs and the mass concentration of PM10 and PM2.5, respectively, as well as their constituents. Higher R values were obtained for all regression equations using MISR component AODs as compared to those using total AOD. MISR component AODs showed higher capacity for monitoring daily BC (R=0.74–0.75) and sulfates (R=0.72), as compared to nitrates (R=0.52–0.54) and hourly OC (R=0.47). The potential of MISR component AODs for ambient PM monitoring should be explored and applied in other regions.

Assessing exposure to ambient air pollution at the urban temporal scale by analysis of image color and effective bandwidth

Airborne particulate matter (PM) attributes have a key role in assessing its health effects, since exposure to PM, especially the fine particle fraction (with diameter smaller than 2.5 ?m, PM2.5), has been associated with adverse effects such as cardiovascular and respiratory diseases. PM2.5 has also longer atmospheric residence time in comparison to the coarser fraction (PM2.5-10), which increases its potential exposure risk. It is therefore important to monitor PM2.5 spatiotemporal variations near the ground at an urban-scale resolution. Satellite remote sensing is a common approach for gathering spatiotemporal data regarding aerosol events but its current spatial resolution is limited to a relatively large grid that does not fit highly varying urban conditions. Moreover, satellite-borne remote sensing has limited revisit periods and it measures along vertical atmospheric columns. Thus, linking satellite-borne aerosol products to ground PM measurements is still extremely challenging. In the last two decades, visibility analysis of horizontal imaging is used by the US Environmental Protection Agency (US-EPA) to obtain quantitative representation of the air quality in rural areas. Significantly fewer efforts have been given to utilize the acquired scene characteristics (color, contrast, etc.) for quantitative parametric modeling of PM concentrations. We suggest utilizing the image effective bandwidth, a quantitative measure of image characteristics, for predicting PM concentrations. For validating the suggested method, we have assembled a large dataset that consists of time series imaging as well as measurements from air quality monitoring stations located in the study area, that report PM concentrations and meteorological data (wind direction and velocity, relative humidity, etc.) Quantitative and qualitative statistical evaluation of the suggested method shows that dynamic changes of PM concentrations can be inferred from the acquired images.

A robust calibration approach for PM&amp;lt;sub&amp;gt;10&amp;lt;/sub&amp;gt; prediction from MODIS aerosol optical depth

Abstract. Investigating the human health effects of atmospheric particulate matter (PM) using satellite data are gaining more attention due to their wide spatial coverage and temporal advantages. Such epidemiological studies are, however, susceptible to bias errors and resulted in poor predictive output in some locations. Current methods calibrate aerosol optical depth (AOD) retrieved from MODIS to further predict PM. The recent satellite-based AOD calibration uses a mixed effects model to predict location-specific PM on a daily basis. The shortcomings of this daily AOD calibration are for areas of high probability of persistent cloud cover throughout the year such as in the humid tropical region along the equatorial belt. Contaminated pixels due to clouds causes radiometric errors in the MODIS AOD, thus causes poor predictive power on air quality. In contrary, a periodic assessment is more practical and robust especially in minimizing these cloud-related contaminations. In this paper, a simple yet robust calibration approach based on monthly AOD period is presented. We adopted the statistical fitting method with the adjustment technique to improve the predictive power of MODIS AOD. The adjustment was made based on the long-term observation (2001–2006) of PM10-AOD residual error characteristic. Furthermore, we also incorporated the ground PM measurement into the model as a weighting to reduce the bias of the MODIS-derived AOD value. Results indicated that this robust approach with monthly AOD calibration reported an improved average accuracy of PM10 retrieval from MODIS data by 50% compared to widely used calibration methods based on linear regression models, in addition to enabling further spatial patterns of periodic PM exposure to be undertaken.

Classification of dust days by satellite remotely sensed aerosol products

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 μm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88% occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2% correct classifications of DD and NDD. Evaluation of the combined CART–logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

Integrated single particle-bulk chemical approach for the characterization of local and long range sources of particulate pollutants

Abstract The feasibility of an integrated approach based on multivariate statistical analysis and individual particle micro-analyses to characterize airborne particulate matter (PM) sampled at urban and regional background sites was tested. The proposed methodology encompasses data from ICP–AES, GC–MS and SEM–EDS analyses on ground and vertical profile PM samples. Source apportionment analysis of bulk chemical data allowed the identification and quantification of five distinct source categories for the fine and coarse particle size ranges. Results of quantitative phase analysis were included in the statistical investigation in order to correlate type and relevance of PM sources with relative abundance and texture of particles at ground level. Results were finally evaluated in the light of back-trajectory calculations and atmospheric vertical profile measurements. This approach allowed to discriminate between local from Saharan dust crustal contributions, and urban from regional secondary aerosol anthropogenic inputs.

Chemical Composition of PM10 and PM2.5 Collected at Ground Level and 100 Meters during a Strong Winter-Time Pollution Episode in Xi'an, China

ABSTRACT An intensive sampling of aerosol particles from ground level and 100 m was conducted during a strong pollution episode during the winter in Xi'an, China. Concentrations of water-soluble inorganic ions, carbonaceous compounds, and trace elements were determined to compare the composition of particulate matter (PM) at the two heights. PM mass concentrations were high at both stations: PM10 (PM with aerodynamic diameter ≤10 μm) exceeded the China National Air Quality Standard Class II value on three occasions, and PM2.5 (PM with aerodynamic diameter ≤2.5 μm) exceeded the daily U.S. National Ambient Air Quality Standard more than 10 times. The PM10 organic carbon (OC) and elemental carbon (EC) were slightly lower at the ground than at 100 m, both in terms of concentration and percentage of total mass, but OC and EC in PM2.5 exhibited the opposite pattern. Major ionic species, such as sulfate and nitrate, showed vertical variations similar to the carbonaceous aerosols. High sulfate concentrations indicated that coal combustion dominated the PM mass both at the ground and 100 m. Correlations between K+ and OC and EC at 100 m imply a strong influence from suburban biomass burning, whereas coal combustion and motor vehicle exhaust had a greater influence on the ground PM. Stable atmospheric conditions apparently led to the accumulation of PM, especially at 100 m, and these conditions contributed to the similarities in PM at the two elevations. Low coefficient of divergence (CD) values reflect the similarities in the composition of the aerosol between sites, but higher CDs for fine particles compared with coarse ones were consistent with the differences in emission sources between the ground and 100 m. IMPLICATIONS An intensive observation of aerosol particles from ground level and 100 m during a winter-time pollution episode in Xi'an, China, showed that PM levels often greatly exceeded national standards. High concentrations of sulfate indicated that emissions from coal combustion dominated the PM mass both at the ground and at 100 m. No dramatic differences in PM or chemical species loadings were found between the different elevations, and this was most likely due to relatively stable meteorological conditions. Nevertheless, the data suggested greater impacts from fresh emissions at the ground site and more particle aggregates and a stronger influence from biomass burning at the higher site. The results imply that monitoring programs need to take this variability into account. The air quality in Xi'an would be improved by reducing the emissions from coal and biomass burning.

Detection of Sahara dust intrusions during mixed advection patterns over south-east Italy: A case study

Measurements from July 4 to July 8, 2005 by a high resolution visible radiometer, a Raman lidar, a ground particulate matter sampler, and ground meteorological sensors have been combined in synergy to infer the intrusion over south-east Italy, of air masses from north-west Sahara, the Atlantic Ocean, and the continental Europe. It is shown that backscatter coefficient, depolarization-ratio, and lidar ratio vertical profiles represent the best tools to detect the intrusion of long range transported air masses and to monitor their effects on the vertical distribution of aerosol optical and microphysical properties. High resolution radiometers are instead important tools to monitor changes on columnar aerosol properties and size distributions. Backscatter coefficient, depolarization-ratio, and lidar ratio vertical profiles have revealed that aerosol optical and microphysical properties significantly changed with time and space during African dust outbreaks: the intrusion of dust particles that at first occurred above 2 km of altitude extending up to 6 km, affected the all aerosol load down to ground within few hours. Aerosol size distributions showed during dust events a clear bimodality with an accumulation mode maximum at 0.24 µm and a coarse mode maximum at 0.94 μm. Conversely, we have found that during the advection of air masses from the Atlantic and continental Europe, aerosol particles were mainly located below 2 km, their optical and microphysical properties were affected by smaller changes in time and space, and were characterized by depolarization ratios rather close to those due to a pure molecular atmosphere. In this case bimodal size distributions with an accumulation mode showing two sub-modes at 0.16 μm and 0.24 μm, respectively and a coarse mode centred at 0.94 μm have also been observed.