PM10 Data Research Articles

Assessing air quality changes in heavily polluted cities during the COVID-19 pandemic: A case study in Xi'an, China

Chinese government has instated strict restrictions to halt the spread of COVID-19. Given the complete shutdown of emission-resources, like traffic, factories, restaurants, and construction sites, responses to this pandemic have wrought unintended consequences in air quality. We assessed air pollution during the COVID-19 pandemic in terms of PM2.5, PM10, and O3 in Xi'an, China, and revealed the relations between air quality and potential emission resources. We gleaned pollutant concentration data of O3, PM2.5, and PM10 from five monitoring sites and identified their trending during the observed periods. We also deployed ArcGIS to interpolate points among data detected by 130 monitoring sites and obtained spatial distribution of pollution during the observed periods. Correlation analysis helped us reveal the relations between pollutants and seven sources. The results showed that during the lockdown section, the concentration of O3 rose by 100.61 %, and those of PM2.5 and PM10 dropped by 22.4 % and 20.7 %, respectively; and during the recovering section, the concentration of PM10 increased by 12.8 %. The spatial distributions also helped us identify two high-polluted areas and two risky areas where PM10 increased sharply. The correlation analysis also implied that decreasing emission sources is the key to improve air quality. Our study also suggests that coordinated control on ozone and particles should be the focus in the future, and the two high-polluted and the two risky areas require immediate administrative interference. Our study can be a valuable reference for public propaganda on green life and governments' sustainable development strategies. The research method for Xi'an might also inspire similar studies on other cities.

Spatial and temporal distribution characteristics and influencing factors analysis of particulate matter pollution in Jinan City

With the development of economy, PM2.5 and PM10 have already become primary air pollutants that seriously affect people’s life and health. As the capital city of Shandong Province, Jinan’s atmospheric environment quality is not optimistic. In the work, hourly PM2.5 and PM10 data were collected in Jinan from 2017 to 2019 to reveal the temporal and spatial variation of local particulate matter and its influencing factors. From 2017 to 2019, the average concentrations of PM2.5 and PM10 decreased by 17.61% and 15.87%, respectively, indicating the effects of efficient control measurements during this period, but which still exceeded the annual limit of the grade II standard. The concentrations of PM2.5 and PM10 were highest in winter and lowest in summer. In addition, the diurnal variation pattern of PM2.5 and PM10 was consistent, which was double peak and double trough pattern. Correlation analysis showed that PM2.5 and PM10 were negatively correlated with temperature and positively correlated with atmospheric pressure, NO2, SO2, and CO, indicating that both meteorological factors and secondary aerosol generation had a strong impact on local particulate pollution. In terms of spatial distribution, both of them showed a general trend that was higher in the north and lower in the south, which was closely related to the distribution of local pollution sources. The results of wavelet analysis showed that PM2.5 and PM10 expressed alternate fluctuation on 34, 69, and 172 days time scales. Long-distance transmission had certain contribution to the local particulate matter concentration, and its potential contribution was mainly from the northern, northwestern, and southwestern areas of Jinan. Through the analysis of atmospheric particulate matter in Jinan city, the results shown herein provide a scientific insight into the impact factors of air pollution in Jinan city.

Evaluation of field calibration methods and performance of AQMesh, a low-cost air quality monitor

Field calibrations of NO2, NO, and PM10 from AQMesh Air Quality Monitors (AQMs) were conducted during a summer and an autumn period in a busy street in a midsize Swedish city. All the three linear calibration procedures studied (postscaled, bisquare, and orthogonal data) significantly reduced the ranges and magnitudes of the performance indicators to yield more reliable results than the raw data. The improvements were sufficient to satisfy the European Union (EU) Data Quality Objective (DQO) for indicative measurements as compared to reference data only for NO2 (above 50 µg m−3) and NO (above 30 µg m−3) during the autumn calibration period. The relatively simple bisquare procedure had the best performance overall. The bisquare procedure improved the root mean square error by the same amount as other studies using complex multivariate calibration methods. Low concentrations of pollutants were measured, far below the EU Environmental Quality Standard thresholds and even satisfying the future goals for the Environmental Quality Objectives. Cleaning the raw data by removing data points in the reference data that were below the reference station limit of detections (and the synchronous data points in the AQM prescaled data) was found to improve the performances of the calibration procedures appreciably. Many NO2 and almost all PM10 data points in this study fell below the AQM limit of detection. These low concentrations will probably be a common problem in many field studies, at least in areas with relatively low air pollution. However, the relative errors were sufficiently low for these data points that they could be interpreted as accurately representing low concentrations and did not need to be removed from the datasets. For the NO2 measurements, a slight periodic error correlated with sunlight and increased ambient temperature was noted. NO measurements correlated strongly with increased traffic.

Spatiotemporal pattern analysis of PM2.5 and the driving factors in the middle Yellow River urban agglomerations

During the development of the Middle Yellow River Urban Agglomerations (Central Plains Urban Agglomeration (CPUA), Guanzhong Plains Urban Agglomeration (GPUA), Jinzhong Urban Agglomeration (JZUA)), “urban disease” was prominent, and the concentrations of PM2.5 in many cities in the region were ultra-high, resulting in serious air pollution. Taking into account the hysteresis of PM2.5 pollution, from the point of view of socioeconomic, this paper uses the geographically and temporally weighted regression (GTWR) model and the latest available data of PM2.5 concentration and socio-economic factors of the urban agglomeration in the Middle Yellow River from 2015 to 2018 to explore the spatiotemporal heterogeneity of PM2.5 concentrations and the driving factors. The results indicate that: (1) The spatiotemporal distribution profiles of PM2.5 concentrations in urban agglomerations in the middle Yellow River have certain regularities. It reveals a U-shaped pattern in each year, and the seasonal changes show the spring and winter are high and summer and autumn are low. (2) PM2.5 in the Middle Yellow River Urban Agglomerations has obvious spatial agglomeration characteristics. (3) There are obvious spatiotemporal heterogeneity in the change of the intensity and direction of each driving factor. Based on the above analysis, three suggestions were put forward: (1) Increase the proportion of new energy in heating. (2) Establish regional joint prevention and control and long-term incentive mechanisms. (3) Different urban agglomerations should formulate differentiated PM2.5 emission reduction strategies.

An assessment of PM1 levels based on indicative PM1 measurements and relationships with PM10 and PM2.5 concentrations, for the analysis of hospital admissions and mortality in the Moravian region.

Particulate matter (PM) air pollution is a serious concern in the city of Ostrava. Thus, in 2018, a project entitled "Validation of the relationships between PM10, PM2.5 and PM1 concentrations, and morbidity and mortality, in the heavily polluted region in the Czech Republic," was launched. The relationship between hospital admissions and mortality in the said region is based primarily on short-term PM10 and PM2.5 concentrations and indicative PM1 measurement. The analysis of spatiotemporal variations and the relationship between PM10, PM2.5 and PM1 data from 3 measurement sites within the city of Ostrava is presented. The analysis was based on the daily average PM concentrations for 5 and 6 months at 2 sites, and on the annual average values (2018-2019) at the baseline station. The correlations of and variability between PM fractions, seasonal differences and explanation of the differences found were the objectives of a detailed analysis. Especially, the potential PM1 variability and its causes were analyzed with respect to the location of the site. The study findings confirmed good correlations between the PM fractions. Compared to PM10, PM2.5 concentrations were more predictive for PM1 concentrations. The annual means of PM10, PM2.5 and PM1 reached 37.5, 29.9 and 27.1 μg/m3 in 2018, respectively, and 25.8, 19.9 and 17.9 μg/m3 in 2019, respectively. The concentration levels in the non-heating season were significantly lower than in the heating season in the 2 years under consideration. The levels of PM10, PM2.5 and PM1 were significantly correlated (the correlation coefficient, r > 0.96). The levels of PM2.5 represented about 0.82-0.86 of PM10, and the levels of PM1 about 0.92-0.93 of PM2.5. These ratios were found to differ in the heating and non-heating seasons, with the PM2.5-PM10 ratio ranging 0.61-0.63 in the non-heating seasons. The correlations found will be used for indicative PM1 measurements in other areas of the region. Seasonal variability should be taken into account as well. Med Pr. 2021;72(3):249-58.

Patterns of relationship between PM10 from air monitoring quality station and AOT data from MODIS sensor onboard of Terra satellite

Thailand, especially in the northern region, often encounters the problem of having PM10 exceeding the normal standard level, which could do harm to people’s health. Mostly, such problem is caused by the burning of forest area and open area; this is clearly seen during January–April of every year. Also, the problem as mentioned is caused by the meteorological conditions and the terrains in the northern region that make it easy for PM10 to be accumulated. The aim of this study was to analyze the patterns of relationship between PM10 measured from the ground monitoring station and AOT data received from MODIS sensor onboard of Terra satellite in Phrae Province located in the northern region of Thailand. The method performed was by analyzing the correlation between PM10 data obtained from the ground monitoring station and the AOT data received from the MODIS sensor onboard of Terra satellite during January–April 2018. It was found from the study that the change of the intensity of PM10 and AOT in the climate was highly related; it appeared that the correlation coefficient (r) in January–April was 0.92, 0.91, 0.91 and 0.92, respectively. This research pointed out that during February– –April, the areas of Phrae Province had the level of PM10 that affected health. Besides, from the method in this research, it revealed AOT data received from MODIS sensor onboard of Terra satellite could be applied in order to follow up, monitor, and notify the spatial changes of PM10 efficiently.

Implementing Citizen Science in Primary Schools: Engaging Young Children in Monitoring Air Pollution

Most European cities have air pollution levels that exceed the threshold for human health protection. Children are sensitive to air pollution and thus it is important to ensure they are not exposed to high concentrations of air pollutants. In order to make a positive change toward cleaner air, a joint effort is needed, involving all civil society actors. Schools and local communities have a decisive role, and can, for example, become engaged in citizen science initiatives and knowledge coproduction. In 2019, with the aim of raising awareness for air quality, NILU developed a citizen science toolbox to engage primary schools in monitoring air quality using a simple and affordable measuring method based on paper and petroleum jelly. This is a very visual method, where the students can clearly see differences from polluted and non-polluted places by looking at “how dirty” is the paper. In addition to the qualitative analysis, we have developed an air meter scale making possible for the students to obtain an indicative measurement of the air pollution level. The comparison between the paper and petroleum jelly method against reference PM10 data collected at two official air quality stations showed a good agreement. The method is a strong candidate for dust monitoring in citizen science projects, making participation possible and empowering people with simple tools at hand. The toolbox is targeted at primary schools and children aged 6–12 years, although it can easily be adapted to other age groups. The main objective of the toolbox is to involve young children who are usually not targeted in air quality citizen science activities, to develop research skills and critical thinking, as well as increase their awareness about the air they breathe. The toolbox is designed to engage students in hands-on activities, that challenge them to create hypotheses, design scientific experiments, draw conclusions and find creative solutions to the air pollution problem. The toolbox includes all the necessary material for the teachers, including guidance, background information and templates facilitating the incorporation in the school curricula. The toolbox was launched as part of the Oslo European Green Capital in March 2019 and was later included as part of the European Clean Air Day initiative coordinated by the European Citizen Science Association (ECSA) working group on air quality. A total of 30 schools and 60 4th grade classes (aged 8–9 years) participated in the Oslo campaign. The citizen science approach employed in the schools, combined the four key elements that promote knowledge integration: elicit ideas, add new ideas, distinguish among ideas and reflect and sort out ideas. Although the main goal of the study was to provide simple but robust tools for engaging young children in air quality monitoring, we also carried out ex-ante and ex-post evaluations in 12 of the participating classes using a 10-question multiple choice test to have an indication of the contribution of the activity to knowledge integration. The results show that there is an increase in the number of correct answers, as well as a reduction in the misconceptions after conducting the activity. These results indicate that applying a citizen science approach improved science instruction and helped knowledge integration by including students' views and taking advantage of the diverse ideas students generated. Citizen science gives learners an insight into the ways that scientists generate solutions for societal problems. But more important, citizen science provides a way to differ from the classic view of the learner as an absorber of information, by considering the social context of instruction and making the topic personally relevant.

Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010-2017 in South Africa.

Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.

Epidemiological Correlation of Pulmonary Aspergillus Infections with Ambient Pollutions and Influenza A (H1N1) in Southern Taiwan.

An increase in fungal spores in ambient air is reported during a spike in particulate matter (PM2.5 and PM10) aerosols generated during dust or smog events. However, little is known about the impact of ambient bioaerosols on fungal infections in humans. To identify the correlation between the incidence of pulmonary aspergillosis and PM-associated bioaerosols (PM2.5 and PM10), we retrospectively analyzed data between 2015 and 2018 (first stage) and prospectively analyzed data in 2019 (second stage). Patient data were collected from patients in three medical institutions in Tainan, a city with a population of 1.88 million, located in southern Taiwan. PM data were obtained from the Taiwan Air Quality Monitoring Network. Overall, 544 non-repeated aspergillosis patients (first stage, n = 340; second stage, n = 204) were identified and enrolled for analysis. The trend of aspergillosis significantly increased from 2015 to 2019. Influenza A (H1N1) and ambient PMs (PM2.5 and PM10) levels had significant effects on aspergillosis from 2015 to 2018. However, ambient PMs and influenza A (H1N1) in Tainan were correlated with the occurrence of aspergillosis in 2018 and 2019, respectively. Overall (2015–2019), aspergillosis was significantly correlated with influenza (p = 0.002), influenza A (H1N1) (p < 0.001), and PM2.5 (p = 0.040) in Tainan City. Using a stepwise regression model, influenza A (H1N1) (p < 0.0001) and Tainan PM10 (p = 0.016) could significantly predict the occurrence of aspergillosis in Tainan. PM-related bioaerosols and influenza A (H1N1) contribute to the incidence of pulmonary aspergillosis.

The impacts of COVID-19 lockdown on PM10 and SO2 concentrations and association with human mobility across Turkey

The SARS-CoV-2 virus pandemic (COVID-19) has caused 2.25 million deaths worldwide by February 3, 2021 (JHU, 2021) and still causing severe health and economic disruptions with increasing rates. This study investigates the impact of lockdown measures on ambient air pollution and its association with human mobility in 81 cities of Turkey. We conducted a countrywide analysis using PM10 and SO2 measurement data by the Turkish Ministry of Environment and Urbanization and mobility data derived from cellular device movement by Google. We observed the most significant change in April 2020. PM10 and SO2 concentrations were lower in 67% and 59% of the cities, respectively in April 2020 compared to the previous five years (2015–2019). The correlation results show that Restaurant/Café, Transit, and Workplaces mobility is significantly correlated with PM10 and SO2 concentration levels in Turkey. This study is the first step of a long-term investigation to understand the air quality impacts on population susceptibility to COVID-19.

Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model

Improving air quality is an important environmental challenge of our time. Chile currently has one of the most stable and emerging economies in Latin America, where human impact on natural resources and air quality does not go unperceived. Santiago, the capital of Chile, is one of the cities in which particulate matter (PM) levels exceed national and international limits. Its location and climate cause critical conditions for human health when interaction with anthropogenic emissions is present. In this paper, we propose a predictive model based on bivariate regression to estimate PM levels, related to PM2.5 and PM10, simultaneously. Birnbaum-Saunders distributions are used in the joint modeling of real-world PM2.5 and PM10 data by considering as covariates some relevant meteorological variables employed in similar studies. The Mahalanobis distance is utilized to assess bivariate outliers and to detect suitability of the distributional assumption. In addition, we use the local influence technique for analyzing the impact of a perturbation on the overall estimation of model parameters. In the predictions, we check the categorization for the observed and predicted cases of the model according to the primary air quality regulations for PM.

The impact of COVID-19 measures on air quality in Turkey

The main objective of this study is to determine the effects of COVID-19 measures on air quality in Turkey. For this investigation, a daily mean of PM10, PM2.5, NO2, CO, O3, and SO2 air pollutant data, provided from the measurement stations run by the Ministry of Environment and Urbanisation and the Istanbul and Izmir Metropolitan Municipalities were used. The R program, an open-code statistics program, was used to generate associated distribution maps using the data obtained. Data for 29 metropolitan cities and the province of Zonguldak, where the pandemic measures were first implemented, were examined for air quality changes. In evaluating whether the COVID-19 measures have had an impact on the air quality of the 30 given cities, two time periods were selected: a period before the COVID-19 measures were implemented (between January 1 and March 15) and the period in which the measures were in force (between March 16 and April 15). In order to take the effects of seasonal conditions on air quality based on the 2020 post-epidemic data into account, the average data from 2018 and 2019 were also compared. This study finds that the restrictions imposed during the COVID-19 pandemic period in the 30 major cities, in which most of the Turkish population lives, significantly improved the air quality. Assuming that some of the measures taken during the pandemic period will become permanent over time (such as the increase in home working, the decrease in air travel thanks to online meetings, the widespread use of distance education, the change in consumption habits and a reduction of waste), the epidemic period is thought to represent a turning point in increasing global air quality.

In-flight particulate matter concentrations in commercial flights are likely lower than other indoor environments.

Air quality in indoor environments can have significant impacts on people's health, comfort, and productivity. Particulate matter (PM; also referred to as aerosols) is an important type of air pollutant, and exposure to outdoor PM has been associated with a variety of diseases. In addition, there is increasing recognition and concern of airborne transmission of viruses, including severe acute respiratory syndrome corona-virus 2 (SARS-CoV-2), especially in indoor environments. Despite its importance, indoor PM data during the COVID-19 pandemic are scarce. In this work, we measured and compared particle number and mass concentrations in aircraft cabins during commercial flights with various indoor environments in Atlanta, GA, during July 2020, including retail stores, grocery stores, restaurants, offices, transportation, and homes. Restaurants had the highest particle number and mass concentrations, dominated by cooking emissions, while in-flight aircraft cabins had the lowest observed concentrations out of all surveyed spaces.

Analysis of the Air Pollution Index and Meteorological Factors and Risk Assessment for Tibet

An analysis and statistical evaluation of the environmental air quality, meteorological conditions, and human health effects were carried out for major cities and towns in Tibet. The study selected PM10, SO2, NO2, PM2.5, O3, and CO atmospheric pollutant monitoring data, a pollutant index, and the air quality index for seven cities and towns in Tibet in the year 2017 and for the Lhasa urban area in the period from June 2000 to December 2017. The data were combined with meteorological elements, namely precipitation, temperature, wind, and total solar radiation, for the same period. Results show that the air quality index is highest in Nag Qu, among of the seven cities and towns, followed by Shigatse, which roughly reflects the characteristics of pollution emissions dominated by biomass fuels in agricultural and pastoral areas of Tibet, a lack of vegetation, strong winds and less rain, and increased particle concentration. These factors affects human health. The six pollutants had higher concentrations in the winter half of the year (November to April) and lower concentrations in the summer half of the year (May to October); i.e., the concentration of pollutants was 38% higher in the winter half of the year. The change trend of the six pollutants has changed over the years; the concentrations of NO2 and PM2.5 have increased, the concentration of PM10 has decreased, and the concentrations of O3 and SO2 have not changed appreciably. According to national air quality standards, the percentage number of days with excellent air quality was 22.7%, the percentage number of days with good air quality was 62.7%, the percentage number of days with light pollution was 11.3%, and the percentage number of days with moderate or heavy pollution was 0.6%. Meteorological factors such as precipitation, wind speed, visibility, and total solar radiation affect air pollution.

Three-Hour-Ahead of Multiple Linear Regression (MLR) Models for Particulate Matter (PM10) Forecasting

The increase of air pollutants emission through anthropogenic activities and natural phenomena in the atmosphere can give an adverse impact on human health especially to some groups of people such as children, the elderly, and people that have cardiovascular problems. Multiple Linear Regression (MLR) model establishments for the particulate matter (PM10) forecasting can be useful, as it provides early warning information to the local authorities and the communities. We aim to develop MLR models for PM10 forecasting in Peninsular Malaysia, specifically in the southern part. In this study, the hourly data of PM10, meteorological factors, and gaseous pollutants from the year 2009-2011 had been used. As a result, the next first hour of the MLR prediction model, PM10,t+1 has been selected as the best-fitted model as compared to the second and third prediction hour models, PM10,t+2, and PM10,t+3, respectively. The PM10,t+1 model was explained 61.4% (R2=0.614) variance in the data which is higher compared to model PM10,t+2 and PM10,t+3 with 42.3% (R2=0.423) and 34.7% (R2=0.347), respectively. Thus, the validation of PM10, t+1 model also has a high accuracy value of R2 (55.1%) as compared to the other two models. We conclude that the development of MLR models is adequate for PM10 forecasting in the industrial area.

Seasonal Variation in Chemical Composition of Size-Segregated Aerosols Over the Northeastern Arabian Sea

Water-soluble species constitute a significant fraction (up to 60–70%) of the total aerosol loading in the marine atmospheric boundary layer (MABL). The “indirect” effects, that is, climate forcing due to modification of cloud properties depend on the water-soluble composition of aerosols. Thus, the characterization of aerosols over the MABL is of greater relevance. Here, we present 1-year long aerosol chemical composition data of PM10 and PM2.5 at a costal location in the northeastern Arabian Sea (Goa; 15.45°N, 73.20°E, 56 m above the sea level). Average water-soluble ionic concentration (sum of anion and cation) is highest (25.5 ± 6.9 and 19.6 ± 5.8 μg·m−3 for PM10 and PM2.5, respectively) during winter season and lowest during post-monsoon (17.3 ± 9.1 and 14.4 ± 8.1 μg·m−3 for PM10 and PM2.5, respectively). Among water-soluble ionic spices, SO42- ion was found to be dominant species in anions and NH4+ is dominant in cations, for both PM10 and PM2.5 during all the seasons. These observations clearly hint to the contribution from anthropogenic emission and significant secondary inorganic species formation. Sea-salt (calculated based on Na+ and Cl−) concentration shows significant temporal variability with highest contribution during summer seasons in both fractions. Sea-salt corrected Ca2+, an indicator of mineral dust is found mostly during summer months, particularly in PM10 samples, indicates contribution from mineral dust emissions from arid/semiarid regions located in the north/northwestern India and southwest Asia. These observations are corroborated with back-trajectory analyses, wherein air parcels were found to derive from the desert area in summer and Indo-Gangetic Plains (a hot spot for anthropogenic emissions) during winter. In addition, we also observe the presence of nss-K+ (sea-salt corrected), for PM2.5, particularly during winter months, indicating influence of biomass burning emissions. The impact on aerosol chemistry is further assessed based on chloride depletion. Chloride depletion is observed very significant during post-monsoon months (October and November), wherein more than 80 up to 100% depletion is found, mediated by excess sulfates highlighting the role of secondary species in atmospheric chemistry. Regional scale characterization of atmospheric aerosols is important for their better parameterization in chemical transport model and estimation of radiative forcing.

Health-based evaluation of ambient air measurements of PM2.5 and volatile organic compounds near a Marcellus Shale unconventional natural gas well pad site and a school campus

BackgroundLimited air monitoring studies with long-term measurements during all phases of development and production of natural gas and natural gas liquids have been conducted in close proximity to unconventional natural gas well pads.ObjectiveConducted in an area of Washington County, Pennsylvania, with extensive Marcellus Shale development, this study investigated whether operations at an unconventional natural gas well pad may contribute to ambient air concentrations of potential health concern at a nearby school campus.MethodsAlmost 2 years of air monitoring for fine particulate matter (PM2.5) and volatile organic compounds (VOCs) was performed at three locations between 1000 and 2800 feet from the study well pad from December 2016 to October 2018. PM2.5 was measured continuously at one of the three sites using a beta attenuation monitor, while 24-h stainless steel canister samples were collected every 6 days at all sites for analysis of 58 VOCs.ResultsMean PM2.5 concentrations measured during the different well activity periods ranged from 5.4 to 9.5 μg/m3, with similar levels and temporal changes as PM2.5 concentrations measured at a regional background location. The majority of VOCs were either detected infrequently or not at all, with measurements for a limited number of VOCs indicating the well pad to be a source of small and transient contributions.SignificanceAll measurement data of PM2.5 and 58 VOCs, which reflect the cumulative contributions of emissions from the study well pad and other local/regional air pollutant sources (e.g., other well pads), were below health-based air comparison values, and thus do not provide evidence of either 24-hour or long-term air quality impacts of potential health concern at the school.

Air Pollution Assessment in China: A Novel Group Multiple Criteria Decision Making Model under Uncertain Information

Assessment of and controlling air pollution are urgent global issues where international cooperation is deemed necessary. Although a very relevant data source can be obtained through continuous monitoring of air quality, measuring air pollutant concentrations is quite difficult when compared to other environmental indicators. We mainly have three different aims for the current study: (1) we propose the computation of the interval weights of decision makers (DMs) based on a group multiple criteria decision making (GMCDM) model; (2) we aim to rank the overall preferences of DMs by the possibility concepts; (3) we aim to evaluate the air quality in China using the most recent data based on our proposed method. We consider three monitoring stations, namely Luhu Park, Wanqingsha, and Tianhu, and the data for SO2, NO2, and PM10 are collected for November 2017, 2018, and 2019. The results from our innovative model show that November 2019 had the best air quality. Finally, robustness analyses are also performed to confirm the discriminatory power of the proposed approach.

Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for (18.4%), (18.5%), (57.4%), (19.0%), and (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

Dispersion modeling of PM10 from selected flow stations in the Niger Delta, Nigeria: implications on soot pollution

BackgroundGas flaring in the Niger Delta releases particles which are dispersed over a wide area and have impacts on the environment and human health. The study aimed at assessing the extent of dispersion of PM10 emitted from gas flares in flow stations. Eight selected flow stations in Rivers and Bayelsa states were investigated. The concentrations of PM10 emitted from the flare stacks were monitored 60 m away from the flare stack using a hand-held Met One AEROCET 531 combined Mass Profiler and Particle Counter. Meteorological parameters such as wind speed, ambient temperature and relative humidity were monitored during the sampling campaign. PM10 and meteorological data were analysed for simple and descriptive statistics using SPSS for Windows (version 21.0). Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was adopted to predict the dispersion of PM10 from the flow stations.ResultsResults revealed the range concentrations of PM10 from the flow stations (FS 1–8) as 19.9 µg/m3 at FS 1 to 55.4 µg/m3 at FS 8. The maximum concentration of PM10 at FS 8 was higher than the World Health organisation limit of 50 µg/m3. The dispersion of PM10 emitted from FS 1, 4 and 7 in April 2017, had a fitting spread over Port Harcourt City.ConclusionsThe modeling results revealed dispersion of PM10 from the flow stations to 14 states in Nigeria. This suggests possible detrimental health and environmental effects of PM10 on residents in the identified states.