PM10 Data Research Articles

Source apportionment of ambient PM10 collected at three sites in an urban-industrial area with multi-time resolution factor analyses

Chemical speciation data for PM10, collected for annual trend analyses of health-relevant species, at three receptor sites in a highly industrialized area (IJmond) in the Netherlands were used in a multi-time resolution receptor model (ME-2) to identify the PM10 sources in this area. Despite the available data not being optimized for receptor modelling, five-factor solutions were obtained for all sites based on independent PMF analysis on PM10 data from the three sites (IJM, WAZ and BEV). Four factors were common to all three sites: nitrate-sulphate (average percentage contributions to PM10: IJM: 35.3 %, WAZ: 37.7 %, and BEV: 36.3 %); sea salt (20.2 %, 23.7 %, 15.2 %); industrial (8.1 %, 11.0 %, 18.1 %) and brake wear/traffic (31.4 %, 21.2 %, 20.6 %). At WAZ, a local/site-specific factor containing most of the PAH measurements was found (6.4 %) while a crustal matter factor was resolved at IJM (7.6 %) and BEV (9.8 %). Additionally, sludge-drying was a potential source of the marker species in the industrial factor at WAZ. Bootstrapping (BS) and factor displacement (DISP) were applied to the factor profiles in this work for error estimation. In general, the factor profiles at all three sites had very small intervals from both BS and DISP methods. To our knowledge, this is the first time DISP was applied in a complex model such as the multi-time resolution model. Most of the measured metal and PAH concentrations found in the IJmond area during the 2017–2019 period had local sources, with significant contributions from several processes related to the steel industry. This study shows that available detailed PM10 chemical speciation data, although primarily collected for annual trend analyses of health-relevant species, could also be used in receptor modelling by applying a multi-time framework. We propose general recommendations for the optimization of the measurement strategy for source apportionment of PM in areas with similar urban-industrial land use.

Long-term trend analysis of criteria pollutants in megacity of Delhi: Failure or success of control policies

The study aimed to analyze the long-term trend of four criteria pollutants (PM2.5 and NO2 during 2006–2019; SO2 and CO during 2000–2019) and the effect of pollution control measures in Delhi. The hourly data of PM2.5 and NO2 was taken from six air quality monitoring stations while SO2 and CO data was retrieved from MERRA reanalysis (Modern-Era Retrospective Analysis for Research and Applications) products. PM2.5 and NO2 datasets from six monitoring stations were divided into three categories based on their years of data collection. At all the categories of sites, both the pollutants showed three distinct trends including a decreasing trend which was common from 2015 to 2019. The annual average trends of SO2 and CO showed two distinct trends including an increasing trend which was common from 2013 to 2019. The study analyzed various control policies, among them Compressed Natural Gases (CNG) implementation in December 2002, is a historic measure which was followed by 13.5% decrease in particulate matter trend and 20% increase in NO2 trend during 2002–2005 while 7% decrease in CO trend during 2003–2005. The introduction of ultralow sulfur diesel and closure of coal based thermal power plants reduced SO2 levels significantly during 2011–2012.

Ensemble averaging using remote sensing data to model spatiotemporal PM10 concentrations in sparsely monitored South Africa

There is a paucity of air quality data in sub-Saharan African countries to inform science driven air quality management and epidemiological studies. We investigated the use of available remote-sensing aerosol optical depth (AOD) data to develop spatially and temporally resolved models to predict daily particulate matter (PM10) concentrations across four provinces of South Africa (Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape) for the year 2016 in a two-staged approach. In stage 1, a Random Forest (RF) model was used to impute Multiangle Implementation of Atmospheric Correction AOD data for days where it was missing. In stage 2, the machine learner algorithms RF, Gradient Boosting and Support Vector Regression were used to model the relationship between ground-monitored PM10 data, AOD and other spatial and temporal predictors. These were subsequently combined in an ensemble model to predict daily PM10 concentrations at 1 km × 1 km spatial resolution across the four provinces. An out-of-bag R2 of 0.96 was achieved for the first stage model. The stage 2 cross-validated (CV) ensemble model captured 0.84 variability in ground-monitored PM10 with a spatial CV R2 of 0.48 and temporal CV R2 of 0.80. The stage 2 model indicated an optimal performance of the daily predictions when aggregated to monthly and annual means. Our results suggest that a combination of remote sensing data, chemical transport model estimates and other spatiotemporal predictors has the potential to improve air quality exposure data in South Africa's major industrial provinces. In particular, the use of a combined ensemble approach was found to be useful for this area with limited availability of air pollution ground monitoring data.

Evaluating the Robustness of NISAR's Cropland Product to Time of Observation, Observing Mode, and Dithering

AbstractCropland mapping is important for monitoring agricultural practices, cropland distribution and for supporting food security programs. Radar remote sensing will likely provide a means of cropland mapping, which can be efficient, accurate, and globally applied. Algorithms used for spaceborne radar cropland mapping are still undergoing calibration and validation efforts, necessitating ground and aircraft‐based campaigns ahead of launch. The upcoming NASA ISRO SAR (NISAR) mission will map croplands at 1 ha spatial resolution using a coefficient of variation approach. NASA Jet Propulsion Laboratory recently released simulated NISAR data over cropland calibration sites including morning (AM), evening (PM), and dithered with and without gaps. These freely available simulated data allow for making accurate a priori estimates of how NISAR’s cropland science algorithm is impacted by factors such as orbit (i.e., the incidence angle, time of day), bandwidth (i.e., whether the 5, 20, or 40 MHz channels are used), and varying the system's pulse repetition frequency—a process known as dithering. This study investigates these science mission questions over agricultural regions in Mississippi and Arkansas, USA. Results show that compositing data from both orbits (AM + PM) yielded comparable classification accuracy to using only one of AM or PM data. The study also found that dithering only slightly decreased cropland mapping capability (<0.3% accuracy reduction).

Data-Driven Prediction of COVID-19 Daily New Cases through a Hybrid Approach of Machine Learning Unsupervised and Deep Learning

Air pollution is associated with respiratory diseases and the transmission of infectious diseases. In this context, the association between meteorological factors and poor air quality possibly contributes to the transmission of COVID-19. Therefore, analyzing historical data of particulate matter (PM2.5 and PM10) and meteorological factors in indoor and outdoor environments to discover patterns that allow predicting future confirmed cases of COVID-19 is a challenge within a long pandemic. In this study, a hybrid approach based on machine learning and deep learning is proposed to predict confirmed cases of COVID-19. On the one hand, a clustering algorithm based on K-means allows the discovery of behavior patterns by forming groups with high cohesion. On the other hand, multivariate linear regression is implemented through a long short-term memory (LSTM) neural network, building a reliable predictive model in the training stage. The LSTM prediction model is evaluated through error metrics, achieving the highest performance and accuracy in predicting confirmed cases of COVID-19, using data of PM2.5 and PM10 concentrations and meteorological factors of the outdoor environment. The predictive model obtains a root-mean-square error (RMSE) of 0.0897, mean absolute error (MAE) of 0.0837, and mean absolute percentage error (MAPE) of 0.4229 in the testing stage. When using a dataset of PM2.5, PM10, and meteorological parameters collected inside 20 households from 27 May to 13 October 2021, the highest performance is obtained with an RMSE of 0.0892, MAE of 0.0592, and MAPE of 0.2061 in the testing stage. Moreover, in the validation stage, the predictive model obtains a very acceptable performance with values between 0.4152 and 3.9084 for RMSE, and a MAPE of less than 4.1%, using three different datasets with indoor environment values.

Conditions Affecting Wind-Induced PM10 Resuspension as a Persistent Source of Pollution for the Future City Environment

Air pollution by particulate matter (PM) in cities is an ongoing problem with increasing severity. The biggest PM contributors are traffic and domestic fire burning. With the shift towards electromobility and the use of low-emission fuels, attention should be shifted to less mentioned sources of pollution. Such sources of pollution include wind-induced resuspension. This study focuses on determining the threshold wind speed causing the resuspension of particulate matter (TWSR) with aerodynamic diameter smaller than 10 µm. A methodology is introduced that examines how data could be treated to identify its characteristics (for locations where only PM10 data are available). The most significant monitored parameters are air humidity, wind direction, time of the day, and surface type. The characteristic wind speeds causing resuspension are identified in four locations for different times of day. It was proven that at times of intense human activity, particles are lifted by wind more easily. The mean threshold wind speed causing resuspension in the studied urban environment was identified as 1.58 m/s at a height of 2 m above the surface. The wind speeds were also compared with experimental studies of resuspension. The results proved correspondence between the identified wind speeds and the experimental results.

Stringent Emission Controls Are Needed to Reach Clean Air Targets for Cities in China under a Warming Climate.

Quantifying the threat that climate change poses to fine particle (PM2.5) pollution is hampered by large uncertainties in the relationship between PM2.5 and meteorology. To constrain the impact of climate change on PM2.5, statistical models are often employed in a different manner than physical-chemical models to reduce the requirement of input data. A majority of statistical models predict PM2.5 concentration (often log-transformed) as a simple function of meteorology, which could be biased due to the conversion of precursor gases to PM2.5. We reduced this bias by developing a unique statistic model where the sum of PM2.5 and the weighted precursor gases, rather than the PM2.5 alone, was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM10, CO, O3, NOx, and SO2 were obtained from routine measurements. This modification, without losing the simplicity of statistical models, reduced the mean-square error from 27 to 17% and increased the coefficient of determination from 47 to 67% in the model cross-validation using daily PM2.5 observations during 2013-2018 for 74 cities over China. We found a previously unrecognized mechanism that synoptic climate change in the past half-century might have increased low quantiles of PM2.5 more strenuously than the upper quantiles in large cities over China. Climate change during 1971-2018 was projected to increase the annual mean concentration of PM2.5 at a degree that could be comparable with the toughest-ever clean air policy during 2013-2018 had counteracted it, as inferred from the decline in the daily concentration of carbon monoxide as an inert gas. Our estimate of the impact of climate change on PM2.5 is higher than previous statistical models, suggesting that aerosol chemistry might play a more important role than previously thought in the interaction between climate change and air pollution. Our result indicated that air quality might degrade if the future synoptic climate change could continue interacting with aerosol chemistry as it had occurred in the past half-century.

The Impact of Economic Development of the Guangdong-Hongkong-Macao Greater Bay Area on Air Pollution: Investigation Based on Remote Sensing Data of Nighttime Lights and Air Pollution

Whether the Guangdong-Hongkong-Macao Greater Bay Area (GBA) can achieve the goal of environmental protection at the same time as its rapid economic development is an important issue that deserves attention. In this paper, we obtained remote sensing data on air pollution including CO, NO2, SO2, PM10, PM2.5, and nighttime light data. We combined the remote sensing data with county-level economic statistics to study economic development and air pollution in the Guangdong-Hongkong-Macao GBA. Our research showed that the economic development in the Guangdong-Hongkong-Macao GBA significantly increased the emissions of NO2, PM10, and PM2.5 and did not have significant effects on CO and SO2. We constructed an emission intensity indicator of pollutants per unit of luminance. We found that the unit emission intensity of four categories of pollutants, PM10, PM2.5, SO2, and CO, showed a significant decreasing trend in line with economic development. The emission intensity of NO2 showed a trend of first decreasing and then increasing. The conclusion of this research illustrates that the emission intensity of pollutants per unit of luminosity in the Guangdong-Hongkong-Macao GBA is continuously reducing. The emission of pollutants will gradually change from slow growth to zero growth. However, the emission of NO2 pollution should be an area of focus in achieving the dual goals of economic development and environmental protection.

Analysis of PM2.5 Transmission Characteristics in Main Cities of Jinzhong Basin in Winter

In this study, we analyzed the hourly concentration data of PM10 and PM2.5 in major cities in Jinzhong basin from 2017 to 2019. The main distribution characteristics of aerosols in Jinzhong and Taiyuan were determined, and PM2.5 hourly concentration data and HYSPLIT in Jinzhong basin in winter were discussed. The results showed that the overall level of particulate matter concentration in Taiyuan was higher than that in Jinzhong, and the monthly and seasonal variation characteristics were similar. All showed high concentrations in winter and low concentrations in summer, and the highest concentration value appeared in January. The aerosol pollution caused by the static and stable weather in Jinzhong was more common than that caused by the sand and dust weather in Taiyuan. The distribution of particulate matter showed the characteristics of more intermediate values in Jinzhong and more high and fewer low values in Taiyuan, and winter was the highest incidence season of PM2.5 pollution in Jinzhong basin. PM2.5 transmission passageways in the main cities of Jinzhong basin in winter could be divided into four categories:class 1 was transmitted along the transverse valley of Taihang Mountain, and class 2 was the southeast transmission channel. Class 1 and class 2 were the short-range transmission passageways; air masses carried more moisture, and PM2.5 transmitted along such passageways allowed moisture to be absorbed more easily, increasing levels and aggravating local pollution. Class 3 was the northwest passageway, corresponding to the most serious pollution period of PM2.5 in Jinzhong basin before the arrival of cold air, which also corresponded to the dust transmission passageway. Class 4 was the Fenwei Plain passageway, corresponding to high-concentration PM2.5 pollution. Areas with dense pollution tracks (more than 100 pollution tracks) and areas with slow air flow movement (RTA pollution track end points greater than 50) easily became potential source areas of target cities (PSCF contribution greater than 0.7). The main potential source areas of PM2.5 in winter in Jinzhong (PSCF contributing more than 0.7) were mainly distributed in Linfen, Jincheng, and other places in Shanxi province, as well as in the north of Henan province, the south of Hebei province, and central and south Shaanxi province. The distribution range of main potential source areas of PM2.5 in Taiyuan in winter was wider than that in Jinzhong, including the south of Lvliang, Yangquan, Linfen, and Yuncheng and the south of Jinzhong in Shanxi, as well as most areas in southern Shaanxi, northern Henan province, and southern Hebei province. In addition, the PSCF distribution of high-value centers above 0.9 was wider than that of Jinzhong. When pollution occurs in cities that PSCF contributed more than 0.9, special attention should be paid to the influence of mutual transmission between them and cities in Jinzhong basin. Jinzhong and Taiyuan showed different distribution characteristics corresponding to the surface wind direction when light and higher pollution occur, when the wind direction near the ground in Jinzhong was E, the frequency of light and higher pollution was 8.1%; it was the highest in all wind directions. When the wind direction near the ground in Taiyuan was SSW, the frequency of light to higher polluted weather was the highest in all wind directions (5.1%). In the case of calm wind, the frequency of light to higher pollution in Taiyuan (3.4%) was higher than that in Jinzhong (0.5%).

Internet of Things Based Data Acquisition Module Design for Air Quality in Public Transport Vehicles

In this study, an ARM-based data acquisition module is designed with the Internet of Things in public transportation vehicles for air
 quality analysis. The designed module communicates with the driver's computer in the vehicle. TEMPerHUM USB Thermometer
 Hygrometer Sensor is used to collect temperature and humidity data and a dust sensor is used as PM2.5 and PM10 sensors. The data
 obtained from these sensors are sent to the microprocessor with the RS-485 port. Microsoft Azure Hub is used to save all data from
 the microprocessor in real-time. Machine learning algorithms are used to evaluate regression models constituting the temperature,
 humidity, and PM data. Regression models are generated in the Python Language. Results of the R2
 
 score and RMSE are found for the
 
 different regression models. The results are assessed and represented.

Assessment of Correlation between PM10 Data Measured at Ground Station of Sanandaj and AOD Data of MODIS Sensor

تحقیق حاضر با هدف ارزیابی دقت داده‌های ماهواره‌ای سنجندة مادیس در پایش ریزگردها (ذرات PM10 )، به‌منظور مقایسه با داده‌های ایستگاه زمینی سنجش آلودگی در شهر سنندج انجام گرفته است. بدین‌ترتیب، میزان عملکرد داده‌های ماهواره‌ای در اندازه‌گیری ریزگردها، در ایستگاه زمینی سنندج، مشخص می‌شود. ابتدا داده‌های ماهواره‌ای عمق نوری (ذرات PM10 ) سنجندۀ مادیس، متناظر با داده‌های PM10 زمینی تهیه‌شده از ایستگاه زمینی پایش آلودگی واقع در شهر سنندج، به‌دست آمد؛ آنگاه ضریب همبستگی دو سری داده محاسبه شد. برای پیش‌بینی دقیق داده‌های PM10، دو مدل آریما و شبکة عصبی مصنوعی به‌کار رفت. داده‌های AOD سنجندة مادیس با استفاده از روش حداکثر برآورد احتمال و وزن به‌دست‌آمده از ریشة میانگین مربعات خطا، به‌منظور استفاده در این دو مدل، ترکیب شدند. در نهایت، روش مقایسۀ منفرد برای هریک از مدل‌ها و نیز مقایسۀ مدل‌ها، با هدف شناسایی مدل بهتر در تشخیص و پیش‌بینی داده‌های PM10 حاصل از سنجندۀ مادیس، اعتبارسنجی شد. در مدل شبکۀ عصبی، ضریب همبستگی در مرحلۀ آموزش 52%، در مرحلۀ آزمون 53%، RMSE برابر با 62/1 و MAE برابر 62/2 به‌دست آمد. طبق محاسبات، مدل آریمای 1-0-3 تنها مدل مورد قبول با R برابر با 46/0و 06/0MAE= و 69/0RMSE= است. این بیان می‌کند مدل آریما مدل مناسبی برای پیش‌بینی داده‌هاست اما دقت مدل شبکۀ عصبی، در ارزیابی میزان همبستگی بین داده‌ها، بیشتر تشخیص داده شد. نتایج تحقیق نشان داد که بین داده‌های عمق نوری ریزگرد سنجندۀ‌ مادیس با داده‌های زمینی رابطۀ مستقیمی وجود دارد و این الگوریتم قادر به شناسایی گردوغبار است و می‌تواند جایگزین مناسبی برای محصولات PM10 تولیدشده از سوی ایستگاه زمینی باشد.

Variation of Aerosol Optical Depth Measured by Sun Photometer at a Rural Site near Beijing during the 2017–2019 Period

In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and low uncertainty. Research sites are mostly concentrated in cities, while the long-term analysis of aerosol optical depth (AOD) for the pollution transmission channel in rural Beijing is still lacking. Here, we obtained an AOD monitoring dataset from August 2017 to March 2019 using the ground-based CE-318 sun photometer at the Gucheng meteorological observation site in southwest Beijing. These sun photometer AOD data were used for the ground-based validation of MODIS (Moderate Resolution Imaging Spectroradiometer) and AHI (Advanced Himawari Imager) AOD data. It was found that MODIS and AHI can reflect AOD variation trends by sun photometer on daily, monthly, and seasonal scales. The original AOD measurements of the sun photometer show good correlations with satellite observations by MODIS (R = 0.97), and AHI (R = 0.89), respectively, corresponding to their different optimal spatial and temporal windows for matching with collocated satellite ground pixels. However, MODIS is less stable for aerosols of different concentrations and particle sizes. Most of the linear regression intercepts between the satellite and the photometer are less than 0.1, indicating that the errors due to surface reflectance in the inversion are small, and the slope is least biased (AHI: slope = 0.91, MODIS: slope = 0.18) in the noon period (11 a.m.–2 p.m.) and most biased in summer (AHI: slope = 0.77, MODIS: slope = 1.31), probably due to errors in the aerosol model. The daily and seasonal variation trends between CE-318 AOD measurements in the Gucheng site and fine particulate observations from the national air quality site nearby were also compared and investigated. In addition, a typical haze–dust complex pollution event in North China was analyzed and the changes in AOD during the pollution event were quantified. In processing, we use sun photometer and satellite AOD data in combination with meteorological and PM data. Overall, this paper has implications for the study of AOD evolution patterns at different time scales, the association between PM2.5 concentrations and AOD changes, and pollution monitoring.

Restricted Anthropogenic Activities and Improved Urban Air Quality in China: Evidence from Real-Time and Remotely Sensed Datasets Using Air Quality Zonal Modeling

The study aims to examine the major atmospheric air pollutants such as NO2, CO, O3, PM2.5, PM10, and SO2 to assess the overall air quality using air quality zonal modeling of 15 major cities of China before and after the COVID-19 pandemic period. The spatio-temporal changes in NO2 and other atmospheric pollutants exhibited enormous reduction due to the imposition of a nationwide lockdown. The present study used a 10-day as well as 60-day tropospheric column time-average map of NO2 with spatial resolution 0.25 × 0.25° obtained from the Global Modeling and Assimilation Office, NASA. The air quality zonal model was employed to assess the total NO2 load and its change during the pandemic period for each specific region. Ground surface monitoring data for CO, NO2, O3, PM10, PM2.5, and SO2 including Air Quality Index (AQI) were collected from the Ministry of Environmental Protection of China (MEPC). The results from both datasets demonstrated that NO2 has drastically dropped in all the major cities across China. The concentration of CO, PM10, PM2.5, and SO2 demonstrated a decreasing trend whereas the concentration of O3 increased substantially in all cities after the lockdown effect as observed from real-time monitoring data. Because of the complete shutdown of all industrial activities and vehicular movements, the atmosphere experienced a lower concentration of major pollutants that improves the overall air quality. The regulation of anthropogenic activities due to the COVID-19 pandemic has not only contained the spread of the virus but also facilitated the improvement of the overall air quality. Guangzhou (43%), Harbin (42%), Jinan (33%), and Chengdu (32%) have experienced maximum air quality improving rates, whereas Anshan (7%), Lanzhou (17%), and Xian (25%) exhibited less improved AQI among 15 cities of China during the study period. The government needs to establish an environmental policy framework involving central, provincial, and local governments with stringent laws for environmental protection.

EVALUATION OF ALPHASENSE OPC-N2 SENSOR FOR PM10 MEASUREMENT IN THE NORTH JAKARTA

Spatial and temporal data of particulate matter (PM) are limited in Indonesia; hence cost-effective and robust instruments to monitor PM concentration could complete data coverage. The low-cost sensor (LCS) Alphasense OPC-N2 provides real-time PM concentration data and is relatively simple to install and deploy. This paper presents data from an OPC-N2 sensor collocated with a PM10 Beta Attenuation Monitor (BAM). The study was carried out at an air quality monitoring site in North Jakarta belonging to the provincial government. The location is considered suitable for evaluating the performance of OPC-N2 micro sensor as it is a representative of a typical Indonesian urbanized area with a range of pollutant sources, including sea-sourced aerosols. At the same site, a filter-based Gent Sampler (GS) measuring both PM10 and PM2.5 was also deployed. The study showed that 30-minutely and daily average concentrations data for PM10 measured by OPC-N2 were lower than that of BAM measurements in both averaged durations by approximately 50%. The comparison between OPC-N2 and GS for PM10 showed that OPC-N2 measurement was underestimated but it was overestimated for PM2.5. Nonetheless, correlations of OPC-N2 and BAM were 0.530 and 0.607 for PM10 and PM2.5, respectively. These results were comparable to other low-cost sensor evaluation studies in different countries, suggesting that the sensor can represent temporal variation of the reference measurement.

Analysis of Particulate Matter (PM10) Behavior in the Caribbean Area Using a Coupled SARIMA-GARCH Model

The aim of this study was to model the behavior of particles with aerodynamic diameter lower or equal to 10μm (PM10) in the Caribbean area according to African dust seasonality. To carry out this study, PM10 measurement from Guadeloupe (GPE) and Puerto Rico (PR) between 2006 and 2010 were used. Firstly, the missing data issues were addressed using algorithms that we elaborated. Thereafter, the coupled SARIMA-GARCH (Seasonal Autoregressive Integrated Moving Average and Generalized Autoregressive Conditional Heteroscedastic) model was developed and compared to PM10 empirical data. The SARIMA process is representative of the main PM10 sources, while the heteroskedasticity is also taken into account by the GARCH process. In this framework, PM10 data from GPE and PR are decomposed into the sum of the background atmosphere (Bt = anthropogenic activities + marine aerosol), African dust seasonality (St = mineral dust), and extreme events processes (Ct). Akaike’s information criterion (AIC) helped us to choose the best model. Forecast evaluation indexes such as the Mean Absolute Percentage Error (MAPE), the Mean Absolute Scale Error (MASE), and Theil’s U statistic provided significant results. Specifically, the MASE and U values were found to be almost zero. Thus, these indexes validated the forecasts of the coupled SARIMA-GARCH model. To sum up, the SARIMA-GARCH combination is an efficient tool to forecast PM10 behavior in the Caribbean area.

Historical reconstruction of background air pollution over France for 2000–2015

Abstract. This paper describes a 16-year dataset of air pollution concentrations and air quality indicators over France. Using a kriging method that combines background air quality measurements and modeling with the CHIMERE chemistry transport model, hourly concentrations of NO2, O3, PM10 and PM2.5 are produced with a spatial resolution of about 4 km. Regulatory indicators (annual average, SOMO35 (sum of ozone means over 35 ppb), AOT40 (accumulated ozone exposure over a threshold of 40 ppb), etc.) are also calculated from these hourly data. The NO2 and O3 datasets cover the period 2000–2015, as well as the annual PM10 data. Hourly PM10 concentrations are not available from 2000 to 2007 due to known artifacts in PM10 measurements. PM2.5 data are only available from 2009 onwards due to the limited number of measuring stations available before this date. The overall dataset was evaluated over all years by a cross-validation process against background stations (rural, sub-urban and urban) to take into account the data fusion between measurement and models in the method. The results are very good for PM10, PM2.5 and O3. They show an overestimation of NO2 concentrations in rural areas, while NO2 background values in urban areas are well represented. Maps of the main indicators are presented over several years, and trends are calculated. Finally, exposure and trends are calculated for the three main health-related indicators: annual averages of PM2.5, NO2 and SOMO35. The DOI link for the dataset is https://doi.org/10.5281/zenodo.5043645 (Real et al., 2021). We hope that the publication of this open dataset will facilitate further studies on the impacts of air pollution.

PM2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS.

Assessing personal exposure risk from PM2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM2.5 air pollution and public health. The results indicated that the spatial-temporal PM2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.

A holistic approach to the air quality of Konya City, Turkey.

Considering an integrated approach to assess all of the measured pollutants in a diurnal, monthly, seasonal, and annual time scales and understanding the mechanisms hidden under low air quality conditions are essential for tackling potential air pollution issues. Konya, located in central Anatolia, is the largest province of Turkey with a surface area of 40,838 km2 and has different industrial activities. The lack of recent detailed studies limits our information on the underlying air pollution levels in Konya and obscuring policymakers to develop applicable mitigation measures. In this study, we used hourly monitored air quality data of CO, NO2, NOx, PM10, PM2.5, and SO2 from five stations in Konya and investigated the temporal and spatial variabilities for the 2008–2018 period via statistical analysis. Upon analysis, particulate matter was found to be the dominant pollutant deteriorating the air quality of Konya. The highest 2008–2018 periodic mean value of PM10 was found in Karatay Belediye as 70.5 µg/m3, followed by 67.4 µg/m3 in Meram, 58.7 µg/m3 in Selçuklu, and 43.7 µg/m3 in Selçuklu Belediye. The 24-h limit value of PM10 given as 50 µg/m3 in the legislation was violated in all of the stations, mainly during winter and autumn. High positive correlations were found among the stations, and the highest correlation was obtained between Selçuklu Belediye and Karatay Belediye with a Pearson correlation coefficient of 0.77. Long-term data showed a decreasing trend in PM10 concentrations. Diurnal variability is found to be more pronounced than weekly variability. For almost all of the pollutants, except for photochemical pollutants like O3, a prominent result was the nighttime and morning rush hours high-pollutant levels. A case study done for the January 29, 2018 to February 05, 2018 episode showed the importance of meteorology and topography on the high levels of pollution. Limitation of the pollutant transport and dilution by meteorological conditions and the location of Konya on a plain surrounded by high hills are believed to be the main reasons for having low air quality in the region.

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis.

Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O3, NO2, SO2, PM2.5 and PM10 of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM2.5, PM10 and CO are the main pollutants affecting air quality and fluctuation, followed by SO2, NO2 and O3.

Relationship between Particulate Matter (PM2.5) Concentration and Aerosol Optical Depth (AOD) throughout Peninsular Malaysia

Abstract A particular matter (PM2.5) is a small particle in the air that reduces visibility and causes air to fade when raised. It is one of the parameters that contribute to air pollution. There are a few causes of air pollution. It can appear in solid and liquid particles and certain gases that are suspended in the air. Consequently, pollution can significantly and negatively impact human health. This study aimed to explore the relationship between concentrations of PM2.5 and aerosol optical depth (AOD) throughout Peninsular Malaysia using the GIS approach. The tabular data of PM2.5 was acquired from the Department of Environment and overlaid with the MODIS satellite image. A correlation test was performed using QGIS software using Inverse Distance Weighting (IDW) technique. Then, by using the Air Pollution Index data and the value of Aerosol Optical Depth, this study can identify the values that influence the pollutants by studying the relationship between PM2.5 and AOD using MODIS satellite images. The linear regression of PM2.5 concentration with the MODIS AOD has shown significant differences. This study aligns with the eleven Sustainable Development Goals which are focused on sustainable cities and communities, to reduce the adverse environmental impact of cities per capita, especially the air quality such as fine particulate matter (PM2.5 and PM10) in cities.