Ground-based Particulate Matter Research Articles

ANALYSIS OF SPATIAL AND TEMPORAL VARIABILITY OF AEROSOL OPTICAL DEPTH OVER KARABUK USING MODIS

The concept of aerosol refers to the combination of microscopic solid or liquid particles present in the atmosphere along with a mixture of gases. These particles are suspended in the air at different sizes and are evaluated based on their ability to scatter or absorb light, which is quantified through a measurement known as aerosol optical depth. These particles' quantities are determined using specialized devices, commonly referred to as "aerosol optical depth meters" or "optical thickness meters." Additionally, through remote sensing technology, aerosol optical depth can also be measured via satellites. In this study, aerosol optical depth has been examined temporally and spatially in the Karabük province for 2022. For this aim, data from National Air Quality Monitoring Stations (NAQMS) situated nationwide was employed, along with MODIS satellite images. Data from five stations in Karabük province, namely Kardemir1, Kardemir2, Tören Alanı, 75.yıl, and Safranbolu, were used for temporal analysis, while satellite imagery was used for spatial analysis. The relationship between aerosol optical depths derived from MODIS satellite data using green and blue band information and station data was investigated. As a result, a 99% positive correlation was found between the two bands obtained from the MODIS satellite, and a significant correlation was observed between ground-based particulate matter 2.5(PM2.5) and particulate matter 10 (PM10) data. Data from the Tören Alanı station, which had a higher amount of data (357 days) compared to other stations, was used to determine this correlation. It was found that there was an 86.35% positive correlation among particulate matters. A moderate correlation was also identified between ground-based data and aerosol optical depth obtained from satellite imagery.

Simultaneous Use of Ground-Based and Satellite Observation to Evaluate Atmospheric Air Pollution over Amman, Jordan

In this study, a combination of ground-based particulate matter measurements in synergy with space-borne CALIOP lidar recordings, meteorological observations, and reanalysis models have been used to study atmospheric air pollution over Amman, Jordan. The measurement was conducted over a 24-month period spanning from January 2018 to the end of December 2019. The CALIOP aerosol profiles and aerosol layer products version 4.21, level 2, with 5 km horizontal resolution were used to evaluate the vertical structure of the atmospheric constituent over the Amman region. The particle depolarization ratio (PDR) was extracted from CALIOP recordings and has been utilized to classify the type of atmospheric aerosols. This method reveals that the atmosphere above Amman mostly contains three different aerosol types including coarse-mode dust, fine-mode dust (polluted dust), and non-dust aerosols (pollution). Aerosols with 0 < δp≤ 0.075 are categorized as pollution, aerosols with 0.075 < δp≤ 0.20 as polluted dust, and aerosols with 0.20 < δp≤ 0.40 are classified as dust. Both the one- and two-step POlarization-LIdar PHOtometer Networking (POLIPHON) approaches have been applied to the CALIOP aerosol profile product to retrieve the vertical profile of the optical and micro-physical properties of each aerosol type. Lofted-layer top heights and layer thickness in the atmosphere above Amman during the study period were also extracted from the CALIOP aerosol layer products. The highest frequency of occurrence was observed for layers with a top height of 0.5 to 2.5 km with a second smaller peak at 3.5 km. The maximum frequency of the lofted layers (40% of cases) were observed with layer thickness below 0.5 km. For layers with a top height lower than 500 m above ground level, the atmosphere was mostly impacted by polluted dust and pollution aerosols. On the other hand, for layers with a top height above 2500 m agl, the atmosphere was contaminated by depolarizing dust particles.

Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

Measurement and Modelling of Particulate Pollution over Kashmir Himalaya, India

Ground and satellite measurements of particulate pollution play an important role in determining the particulate pollutant-Aerosol Optical Depth (AOD) relationship. The daily observed PM10 and PM2.5 concentration varied from 11–757 μg/m3 and 8–630 μg/m3 with the mean concentrations of 137 ± 119 μg/m3 and 86 ± 90 μg/m3, respectively. The long-term mean annual PM10 and PM2.5 levels are several times higher than the WHO permissible limits. The 1377 satellite-derived AOD observations from the Moderate Resolution Imaging Spectrometer, ground-based particulate matter (PM) and meteorological observations from 2013–2017 were analysed to develop two-variate linear model (TVM) (AOD versus PM10 or PM2.5) and multi-variate regression models (MVMs) (AOD + meteorological parameters versus PM10 or PM2.5) for estimation of the ground level PM10 and PM2.5 in the Kashmir Himalaya, India. The model evaluation showed that the PM predication estimates are significant at 99% confidence level for all the models. The TVM predicts daily PM10 concentration better than PM2.5 explaining 82% and 74% variance in the observed data, respectively. By adding meteorological data to the regression analysis, there is an improvement of 5% and 11% in R2 for PM10 and PM2.5 estimates which inter alia reduced the RMSE by 11.8% and 20.47%, respectively. Estimation of the particulate pollution, utilising satellite-based AOD, observed PM and meteorology, would encourage satellite-based air quality monitoring in the data-scarce Himalaya. However, it is suggested that more studies are required to improve the operational prediction of PM pollution by incorporating satellite observations of other pollutants, and processes in the model using advanced approaches.

Relationship between Remotely Sensed Ambient PM10 and PM2.5 and Urban Forest in Seoul, South Korea

Currently particulate matter (PM) is one of the major threats to public health and safety in urban areas such as Seoul, South Korea. The limited amount of air-quality monitoring systems may not provide sufficient data or coverage, in particular on the spots of urban forest. Considering urban forest as a possible contributor to mitigate PM in an urban area, this study investigated the relationship between the size and topography of urban forests near the air-quality monitoring stations and PM measurements from those stations. The average of PM measurements during the study period of August 2017 to July 2019 was computed into three different domains by using three concentric buffers from 25 monitoring stations distributed across Seoul. To estimate PM concentrations, multiple linear regression models were developed by using satellite-borne multi-spectral band data retrieved from Moderate Resolution Imaging Spectroradiometer onboard Terra (MODIS) and Landsat 8 in conjunction with meteorological data sets. Overall, PM10 and PM2.5 measurements significantly varied with season and tended to be lower with large urban forests than small ones by 5.3% for PM10 and 4.8% for PM2.5. Overall, PM10 and PM2.5 measurements were lower at the domains encompassing high urban forests in elevation than those of relatively flattened forests by 9.1% for PM10 and 3.9% for PM2.5. According to the findings from this study, the topographical difference among urban forests could exert a more significant influence on PM mitigation. The result from correlation analysis between the PM estimates from Landsat 8-based models and ground-based PM measurements was considered reliable based on Pearson’s coefficients of 0.21 to 0.74 for PM10 and −0.33 to 0.74 for PM2.5. It was considered that using a satellite imagery-derived PM model could be effective to manage urban forest over a large area which in general implies the limitation of data collection.

CHARACTERIZATION OF SPRING AIR POLLUTION OF BEIJING IN 2019 USING ACTIVE AND PASSIVE REMOTE SENSING INSTRUMENTS

Abstract. As the capital of China, the Beijing area needs to be paid special attention to its air quality. We used active remote sensing instrument (ground-based lidar), combined with passive remote sensing instrument (VIIRS onboard the NPP spacecraft), to study the serious pollution event over Beijing in spring of 2019. At the same time, the ground-based particulate matter (PM) data and the meteorological element data were analyzed. It is found that the ratio of concentrations of PM2.5 to PM10 is very large during the pollution period. The mean value of ratio is 0.75464, indicated it is fine particulate matter pollution. The Range correction signal (RCS) of lidar is very large in the layer below 0.5 km. But the volume depolarization ratio (VDR) is much less than 0.05. It indicated it is anthropogenic urban aerosols. The change in the aerosol optical depth (AOD) of VIIRS during pollution is consistent with the change in optical properties observed by lidar. The backward trajectory model of HYSPLIT shows that the pollutant came from the Hebei area where industrial pollution is serious, and the local meteorological conditions in Beijing are conducive to the continuous accumulation of pollutants. This work can deepen the understanding of the mechanism of haze formation and can help and support pollution prevention work.

A Physically Based PM$_{\text{2.5}}$ Estimation Method Using AERONET Data in Beijing Area

Over the past few years, regional air pollution has frequently occurred in Mid-Eastern China, especially in Beijing. As the primary pollutant in urban air, atmospheric particulate matter (PM) not only leads to the decrease of atmospheric visibility, but also increases the mortality and morbidity of respiratory system diseases. By analyzing aerosol volume size distribution data downloaded from the AERONET official website, we find that the size distribution of aerosol in Beijing appears a bimodal log-normal structures and parameters of fine mode in AERONET data are mainly contributed by PM $_{\text{2.5}}$ . In this paper, a physically based model is developed to estimate the concentration of PM $_{\text{2.5}}$ , in which, fine mode aerosol optical depth (AOD) at 440, 550, and 675 nm, Effective Radius of the Fine particles, ground-based fine particulate matter (PM $_{\text{2.5}}$ ) data, relative humidity, and boundary layer height data from 2015 to 2016 are used. Those from 2015 are used for calculating integrated extinction efficiencies (〈 Qext 〉) based on the model, and those from 2016 are used for PM $_{\text{2.5}}$ validation. Result shows that R 2 of retrieved PM $_{\text{2.5}}$ against ground-based PM $_{\text{2.5}}$ can reach to 0.70 and RMSE is 33.67 μg/m3 at Beijing site at 440 nm. This study concludes that this method has the potential to retrieve PM $_{\text{2.5}}$ by using AERONET AOD in Beijing, which is independent of ground-based PM $_{\text{2.5}}$ measurement.

Status update: is smoke on your mind? Using social media to assess smoke exposure

Abstract. Exposure to wildland fire smoke is associated with negative effects on human health. However, these effects are poorly quantified. Accurately attributing health endpoints to wildland fire smoke requires determining the locations, concentrations, and durations of smoke events. Most current methods for assessing these smoke events (ground-based measurements, satellite observations, and chemical transport modeling) are limited temporally, spatially, and/or by their level of accuracy. In this work, we explore using daily social media posts from Facebook regarding smoke, haze, and air quality to assess population-level exposure for the summer of 2015 in the western US. We compare this de-identified, aggregated Facebook dataset to several other datasets that are commonly used for estimating exposure, such as satellite observations (MODIS aerosol optical depth and Hazard Mapping System smoke plumes), daily (24 h) average surface particulate matter measurements, and model-simulated (WRF-Chem) surface concentrations. After adding population-weighted spatial smoothing to the Facebook data, this dataset is well correlated (R2 generally above 0.5) with the other methods in smoke-impacted regions. The Facebook dataset is better correlated with surface measurements of PM2. 5 at a majority of monitoring sites (163 of 293 sites) than the satellite observations and our model simulation. We also present an example case for Washington state in 2015, for which we combine this Facebook dataset with MODIS observations and WRF-Chem-simulated PM2. 5 in a regression model. We show that the addition of the Facebook data improves the regression model's ability to predict surface concentrations. This high correlation of the Facebook data with surface monitors and our Washington state example suggests that this social-media-based proxy can be used to estimate smoke exposure in locations without direct ground-based particulate matter measurements.

Particulate matter pollution in the coal-producing regions of the Appalachian Mountains: Integrated ground-based measurements and satellite analysis

ABSTRACTThis study integrates the relationship between measured surface concentrations of particulate matter 10 μm or less in diameter (PM10), satellite-derived aerosol optical depth (AOD), and meteorology in Roda, Virginia, during 2008. A multiple regression model was developed to predict the concentrations of particles 2.5 μm or less in diameter (PM2.5) at an additional location in the Appalachia region, Bristol, TN. The model was developed by combining AOD retrievals from Moderate Resolution Imaging Spectro-radiometer (MODIS) sensor on board the EOS Terra and Aqua Satellites with the surface meteorological observations. The multiple regression model predicted PM2.5 (r2 = 0.62), and the two-variable (AOD-PM2.5) model predicted PM2.5 (r2 = 0.4). The developed model was validated using particulate matter recordings and meteorology observations from another location in the Appalachia region, Hazard, Kentucky. The model was extrapolated to the Roda, VA, sampling site to predict PM2.5 mass concentrations. We used 10 km x 10 km resolution MODIS 550 nm AOD to predict ground level PM2.5. For the relevant period in 2008, in Roda, VA, the predicted PM2.5 mass concentration is 9.11 ± 5.16 μg m-3 (mean ± 1SD).Implications: This is the first study that couples ground-based Particulate Matter measurements with satellite retrievals to predict surface air pollution at Roda, Virginia. Roda is representative of the Appalachian communities that are commonly located in narrow valleys, or “hollows,” where homes are placed directly along the roads in a region of active mountaintop mining operations. Our study suggests that proximity to heavy coal truck traffic subjects these communities to chronic exposure to coal dust and leads us to conclude that there is an urgent need for new regulations to address the primary sources of this particulate matter.

Assessing Long-Term Trend of Particulate Matter Pollution in the Pearl River Delta Region Using Satellite Remote Sensing.

Serious particulate matter (PM) pollution problems in many polluted regions of China have been frequently reported in recent years. Long-term exposure to ambient PM pollution is significantly associated with adverse health effects. Characterizing the long-term trends and variation in PM pollution is a basic requirement for evaluating long-term exposure and for guiding future policies to reduce the effects of air pollution on health. However, long-term, ground-based PM measurements are only available at a few fixed stations. In this study, an algorithm is developed and validated to estimate PM concentrations based on the satellite atmospheric optical depth with 1 km spatial resolution. The long-term trends of PM10 concentrations in the entire Pearl River Delta (PRD) region and different cities are quantified and discussed. From 2001 to 2013, the PM10 pollution of the entire PRD region was dominated by a decreasing trend of -0.15 ± 0.23 μg/m(3)·yr. This decreasing PM10 trend was apparent over 75% of the PRD area, with the most significant decreases observed in the center of the region. However, the remaining 25%, mostly located in the outskirts of the region, showed an increasing PM10 trend. This overall decreasing trend indicates the effectiveness of the control measures applied in the past decade for the primary pollutants.

Particulate matter concentration mapping from MODIS satellite data: a Vietnamese case study

Particulate Matter (PM) pollution is one of the most important air quality concerns in Vietnam. In this study, we integrate ground-based measurements, meteorological and satellite data to map temporal PM concentrations at a 10 × 10 km grid for the entire of Vietnam. We specifically used MODIS Aqua and Terra data and developed statistically-significant regression models to map and extend the ground-based PM concentrations. We validated our models over diverse geographic provinces i.e., North East, Red River Delta, North Central Coast and South Central Coast in Vietnam. Validation suggested good results for satellite-derived PM2.5 data compared to ground-based PM2.5 (n = 285, r2 = 0.411, RMSE = 20.299 μg m−3 and RE = 39.789%). Further, validation of satellite-derived PM2.5 on two independent datasets for North East and South Central Coast suggested similar results (n = 40, r2 = 0.455, RMSE = 21.512 μg m−3, RE = 45.236% and n = 45, r2 = 0.444, RMSE = 8.551 μg m−3, RE = 46.446% respectively). Also, our satellite-derived PM2.5 maps were able to replicate seasonal and spatial trends of ground-based measurements in four different regions. Our results highlight the potential use of MODIS datasets for PM estimation at a regional scale in Vietnam. However, model limitation in capturing maximal or minimal PM2.5 peaks needs further investigations on ground data, atmospheric conditions and physical aspects.

The Added Value of a Proposed Satellite Imager for Ground Level Particulate Matter Analyses and Forecasts

Monitoring aerosols over wide areas is important for the assessment of the population's exposure to health relevant particulate matter (PM). Satellite observations of aerosol optical depth (AOD) can contribute to the improvement of highly needed analyzed and forecasted distributions of PM when combined with a model and ground-based observations. In this paper, we evaluate the contribution of column AOD observations from a future imager on a geostationary satellite by performing an Observing System Simulation Experiment (OSSE). In the OSSE simulated imager, AOD observations and ground-based PM observations are assimilated in the chemistry transport model LOTOS-EUROS to assess the added value of the satellite observations relative to the value of ground-based observations. Results show that in highly polluted situations, the imager AOD observations improve analyzed and forecasted PM2.5 concentrations even in the vicinity of simultaneously incorporated ground-based PM observations. The added value of the proposed imager is small when considering monthly averaged PM distributions. This is attributed to relatively large errors in the imager AODs in case of background aerosol loads coupled to the fact that the imager AODs are column values and an indirect estimate of PM. In the future, model improvements and optimization of the assimilation system should be achieved for better handling of situations with aerosol plumes above the boundary layer and satellite observations containing aerosol profile information. With the suggested improvements, the developed OSSE will form a powerful tool for determining the added value of future missions and defining requirements for planned satellite observations.

In Situ Samplings and Remote Sensing Measurements to Characterize Aerosol Properties over Southeast Italy

Abstract Ground-based particulate matter (PM) samplers, an XeF Raman lidar operating in the framework of the European Aerosol Research Lidar Network (EARLINET), and a sun/sky radiometer operating in the framework of the Aerosol Robotic Network (AERONET) have been used to characterize vertical profiles, optical and microphysical properties, and chemical composition of aerosols during the 29 June–1 July 2005 dust outbreak that occurred over the central-eastern Mediterranean. Aerosol backscatter coefficient, total depolarization, and lidar ratio vertical profiles revealed that a well-mixed dust layer extending from ∼0.5 to 6 km was present over southeastern Italy on 30 June. Sun/sky radiometer measurements revealed a bimodal lognormal size distribution during all measurement days. The particle volume distribution was found to be well correlated either to the PM mass distribution measured at ground by a seven-stage cascade impactor and to the fine to total suspended PM mass ratio measured by ground-based PM samplers. Scanning electron microscopy and ion chromatography analyses on PM samples revealed that coarse-mode aerosols were mainly made of carbonate, aluminum-silicate, and sea salt particles. Carbon, sulfate, and nitrate particles were the main components of fine-mode aerosols representing more than 50% of the total aerosol load; the significant role of fine-mode anthropogenic particles during a dust event is highlighted. Finally, the potential capabilities of complementary measurements by passive and active remote sensing techniques and in situ observations to retrieve the vertical distribution of the particle number and mass concentration are analyzed and discussed.

Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality

Advances in satellite sensors have provided new datasets for monitoring air quality at urban and regional scales. Qualitative true color images and quantitative aerosol optical depth data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite were compared with ground-based particulate matter data from US Environmental Protection Agency (EPA) monitoring networks covering the period from 1 April to 30 September, 2002. Using both imagery and statistical analysis, satellite data enabled the determination of the regional sources of air pollution events, the general type of pollutant (smoke, haze, dust), the intensity of the events, and their motion. Very high and very low aerosol optical depths were found to be eliminated by the algorithm used to calculate the MODIS aerosol optical depth data. Correlations of MODIS aerosol optical depth with ground-based particulate matter were better in the eastern and Midwest portion of the United States (east of 100 � W). Data were patchy and had poorer correlations in the western US, although the correlation was dependent on location. This variability is likely due to a combination of the differences between ground-based and column average datasets, regression artifacts, variability of terrain, and MODIS cloud mask and aerosol optical depth algorithms. Preliminary analysis of the algorithms indicated that aerosol optical depth measurements calculated from the sulfate-rich aerosol model may be more useful in predicting ground-based particulate matter levels, but further analysis would be required to verify the effect of the model on correlations. Overall, the use of satellite sensor data such as from MODIS has significant potential to enhance air quality monitoring over synoptic and regional scales. r 2004 Elsevier Ltd. All rights reserved.