PM10 Concentration Data Research Articles

Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM

Accurate prediction of dust concentration is essential for effectively preventing and controlling mine dust. The environment of opencast mines is intricate, with numerous factors influencing dust concentration, making accurate predictions challenging. To enhance the prediction accuracy of dust concentration in these mines, a combined prediction algorithm utilizing RF-GA-LSSVM is developed. Initially, the random forest (RF) algorithm is employed to identify key features from the meteorological and dust concentration data collected on site, ultimately selecting five indicators—temperature, humidity, stripping amount, wind direction, and wind speed—as the input variables for the prediction model. Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. Additionally, model evaluation metrics and testing methods are established. Compared with LSSVM, PSO-LSSVM, ISSA-LSSVM, GWO-LSSVM, and other prediction models, the GA-LSSVM model demonstrates a final fitting degree of 0.872 for PM2.5 concentration data and 0.913 for PM10 concentration data. The GA-LSSVM model clearly demonstrates a strong predictive performance with low error and high fitting. The research results can serve as a foundation for developing dust control measures in opencast mines.

Initial Insights into Teleworking’s Effect on Air Quality in Madrid City

Commuting to work by private vehicle is one of the main sources of air pollution in cities, mainly from NO2 and particulate matter (PM2.5 and PM10). With the spread of telework, traffic congestion during peak hours is reduced on certain days of the week, improving air quality. This study analyzes the relationship between the improvement of air quality and urban traffic resulting from teleworking activities after the COVID-19 pandemic in Madrid, Spain. This article considers road traffic and teleworking before the COVID-19 pandemic (2018 and 2019), during the pandemic (2020 and 2021) and in the period after (2022 and 2023) in the city center and the influence on certain environmental factors. Daily NO2, PM2.5, PM10, and O3 concentration data were collected at air quality stations in Madrid municipality, and traffic data and some meteorological variables such as wind speed, precipitation and temperature were considered. When conducting correlation and regression analysis among the variables, there is a clear association between NO2 and traffic before the pandemic, which is lower for both PM and O3. This correlation was maintained during the pandemic, except for O3, the association of which increased during this period and then decreased in the later period due to various motives. These results seem to indicate the existence of a relevant relationship between urban mobility and air quality and an especially relevant relationship with telework, suggesting the need for policies aimed at promoting sustainable mobility in the future.

Equivalence of low-cost PM10 concentration measuring devices with a reference method using various correction functions

The aim of the study was to build a corrective model that can be used in the analysed devices and to assess the impact of such a model on the values of the measured concentrations. The novelty of this study is the test of equivalence with the equivalent reference method for hourly data. The study used hourly data of PM10 concentrations measured in a chosen city in Poland. Data was collected from two PM10 sensors and a reference device placed in close proximity. In addition, air temperature, humidity and wind speed were also measured. Among the tested models, a linear model was selected that used primary measurements of PM10, temperature, air velocity, and humidity as the most accurate approximation of the actual PM10 concentration level. The results of the analysis showed that it is possible to build mathematical models that effectively convert PM10 concentration data from tested low-cost electronic measuring devices to concentrations obtained by the reference method.

Transport Influence and Potential Sources of PM2.5 Pollution for Nanjing

In this study, 24-hour backward trajectories of the air mass in Nanjing were calculated by using the HYSPLIT model with the NCEP global reanalysis data from March 2019 to February 2020. The backward trajectories combined with the hourly concentration data of PM2.5 were then utilized in the trajectory clustering analysis and potential pollution source analysis. The results showed that the average concentration of PM2.5 in Nanjing was(36±20) μg·m-3 during the study period, with 17 days exceeding the grade Ⅱ national ambient air quality standards (75 μg·m-3). PM2.5 concentration exhibited clear seasonal variation, with winter (49 μg·m-3)>spring (42 μg·m-3)>autumn (31 μg·m-3)>summer (24 μg·m-3). PM2.5 concentration was significantly positively correlated with surface air pressure but significantly negatively correlated with air temperature, relative humidity, precipitation, and wind speed. Based on the trajectories, seven transport routes were identified in spring, and six routes for the other seasons. The northwest and south-southeast routes in spring, southeast route in autumn, and southwest route in winter were the main pollution transport routes in each season, with the characteristics of short transport distance and slow air mass movement, indicating that local accumulation was one of the main reasons for the high value of PM2.5 in quiet and stable weather. The distance of the northwest route in winter was large, and the PM2.5 concentration was 58 μg·m-3, which was the 2nd highest concentration in all routes, indicating that the cities in the northeast of Anhui had a great transport influence on Nanjing PM2.5. The distribution of PSCF and CWT was relatively consistent, and the main potential source areas were mainly local and adjacent areas of Nanjing, indicating that PM2.5 control is needed to strengthen local control and carry out joint prevention and control with adjacent areas. Winter was most affected by transport, its main potential source area was located at the junction of northwest Nanjing and Chuzhou, and the main source origin was in Chuzhou; therefore joint prevention and control should be expanded to Anhui.

How can trees protect us from air pollution and urban heat? Associations and pathways at the neighborhood scale

With rapidly increasing urbanization, cities frequently suffer from the development of urban pollution and heat islands, the co-occurrence of which increases the vulnerability of cities and threatens public health. Greening practices, particularly the plantation of trees, have become an effective strategy for enhancing the quality of urban environments. However, extensive studies examining the effects of urban greening tend to focus on the singular contribution of trees to mitigating either urban air pollution or heat. In this study, the metropolis of Shanghai, China was selected as a study case to explore associations between particulate matters with aerodynamic diameters ≤10 μm (PM10) and heat mitigation by urban trees. During the summer months, we obtained hourly PM10 concentration and meteorological data from environmental monitoring stations in Shanghai, and land cover data were derived from 0.8-m-resolution GF-2 satellite images. Regression analysis indicated a negative non-linear logarithmic pattern between canopy cover and PM10 or heat indicators within neighborhoods. Mediation analysis confirmed the significant mediatory role of heat mitigation pathways, particularly extreme heat mitigation on PM10 reduction from urban trees. The proportion of indirect PM10mean mitigation effects of urban trees associated with reductions in average temperature, maximum temperature, extreme heat days, and extreme heat hours amounted to 27.11%, 27.59%, 30.61%, and 39.76%, respectively. Different results were obtained when using PM10max as the dependent variable, with only extreme heat events (EHEs) pathways being significant, whereas the proportion of indirect effects associated with extreme heat days and extreme heat hours accounted for 25.89% and 37.08%, respectively. Overall, mitigating extreme heat events makes a larger contribution to air purification than temperature regulation. Our findings enrich and extend the current understanding of associations between the air purification and cooling effects of urban trees, and highlight the potential co-benefits for effectively dealing with air pollution and urban heat in high-density cities.

Concentration prediction and spatial origin analysis of criteria air pollutants in Shanghai

Severe air pollution events still occur frequently in Shanghai. In order to predict when Shanghai air quality satisfies the National Ambient Air Quality Standards of China (NAAQSC) and identify potential source areas of criteria air pollutants for the regional joint prevention and control of air pollution, concentration data of PM2.5, PM10, SO2, NO2 and O3 were collected in 2014–2022 at fourteen monitoring sites across Shanghai and surrounding areas. A first – order rate equation with harmonic regression analysis was employed for time series analysis and concentration prediction. Decreasing concentrations were observed widely over all sites except O3 and NO2. It is very likely that the secondary NAAQSC standards for PMx, and SO2 would be met by 2025 and O3 and NO2 would likely become the critical pollutants that determine air quality level after 2025. Regional transport was predominant for PMx and SO2 pollution. A 3D – CWT multisite joint location method was developed to identify their potential source areas at different spatial resolutions. Weighting function correction was assigned via information entropy of endpoint numbers in each cell. A probabilistic parameter WIPSA was proposed to quantify and normalize the probability that grid cells are source areas in order to achieve fourteen – site joint location, and it was comparable and compatible at different spatial resolutions. Potential source areas of PM2.5 and PM10 were similar, including Henan, Shandong, Hebei and Anhui, while origin domains of SO2 mainly covered Henan and Hebei. In all seasons, air pollution that was transported to Shanghai (i.e., PMx and SO2) originated mainly from the North China Plain; the contribution of marine sources was neglectable.

Inverse modeling of the 2021 spring super dust storms in East Asia

Abstract. Last spring, super dust storms reappeared in East Asia after being absent for one and a half decades. The event caused enormous losses in both Mongolia and China. Accurate simulation of such super sandstorms is valuable for the quantification of health damage, aviation risks, and profound impacts on the Earth system, but also to reveal the climatic driving force and the process of desertification. However, accurate simulation of dust life cycles is challenging, mainly due to imperfect knowledge of emissions. In this study, the emissions that lead to the 2021 spring dust storms are estimated through assimilation of MODIS AOD and ground-based PM10 concentration data simultaneously. With this, the dust concentrations during these super storms could be reproduced and validated with concentration observations. The multi-observation assimilation is also compared against emission inversion that assimilates AOD or PM10 concentration measurements alone, and the added values are analyzed. The emission inversion results reveal that wind-blown dust emissions originated from both China and Mongolia during spring 2021. Specifically, 19.9×106 and 37.5×106 t of particles were released in the Chinese and Mongolian Gobi, respectively, during these severe dust events. By source apportionment it was revealed that the Mongolian Gobi poses more severe threats to the densely populated regions of the Fenwei Plain (FWP) and the North China Plain (NCP) located in northern China than does the Chinese Gobi. It was estimated that 63 % of the dust deposited in FWP was due to transnational transport from Mongolia. For NCP, the long-distance transport dust from Mongolia contributes about 69 % to the dust deposition.

IDENTIFICATION ASSOCIATION BETWEEN PM10 AND HOTSPOT DISTRIBUTION DURING 2019 PEATFIRE EPISODE IN SOUTH SUMATRA PROVINCE, INDONESIA

Exposure to air pollution from peat fires causes serious environmental issues. Peat fires occurred frequently and emitted particulates in some areas in Indonesia, including South Sumatra Province. Hotspots were commonly found in several areas, notably during the dry season. Objective of this study aims to elaborate the association between wildfire and PM10spatially and the possibility of wildfire contribution to PM10 concentration during the 2019 dry season (June to October). The PM10 concentration, meteorological data (wind direction and wind speed), and hotspot data had been collected. To address hotspot distribution from peat fire and large scale of wind direction in South Sumatra, the spatial analysis was used meanwhile the meteorological and PM10 concentration were analyzed using RStudio by windrose and CBPF method. The provided PM10 concentration data had violated national ambient air quality standard for 11 days. The detected hotspot area was distributed dominantly in OKI (Ogan Komering Ilir) Regency. The CBPF result and spatial analysis were found that the most probable source was likely coming from South East to East direction. The source was predicted from OKI Regency with a big portion of hotspots area (>50%). The study showed a good probability (0.6) that the prediction of the hotspot in OKI Regency contributed spatially and statistically to PM10 concentration for receptor location in Palembang City.

Haze Prediction Model Using Deep Recurrent Neural Network

In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes.

A new hybrid fuzzy time series model with an application to predict PM10 concentration

Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.

Dust Criteria Derived from Long-Term Filter and Online Observations at Gosan in South Korea

Dust and pollution are frequently mixed together in East Asia, causing large uncertainties in assessing climate change and environmental influence and in relevant policymaking. To discern the dust effect on particle mass, we carried out long-term measurements of the mass and key chemical compositions of PM10, PM2.5, and PM1 from August 2007 to February 2012 and collected hourly data of PM10 and PM2.5 concentrations from January 2012 to October 2020 at Gosan, South Korea. The principal component analysis of measured species reveals two dominant factors, pollution and dust, accounting for 46% and 16% of the total variance, respectively. The mode distribution of PM10, PM2.5, and PM1 mass in addition to the dust events helps to provide a robust criterion of the dust impact. Dust can be identified by the mean + standard deviation (σ) of PM10, while the threshold is down to the mean concentration when dust particles experience precipitation. High PM2.5 concentration also presents dust impact; however, the criterion decreases from mean + σ in 2007–2012 to mean in 2012–2020. It indicates that dust is no longer a high-concentration event of PM2.5, but its influence gradually appears in low-concentration particles. Therefore, the dust criterion obtained from long-term PM10 concentration data is robust; however, the standard is based on PM2.5 changes over time and still needs to be determined by follow-up long-term observations.

Evaluating the Computational Values of Suspended Particles Below 10 μm With Their Actual Concentration

Background & Aims of the Study: Dust as a phenomenon is among the significant problems in numerous regions of Iran and other countries. Empirical equations can be used to estimate the concentration of particulate matter. This study evaluated computational values of suspended particles less than 10 μm with their actual concentration in Ahvaz city based on a linear regression model. Materials and Methods: This descriptive cross-sectional study was conducted from April 2016 to March 2018. The dust images were obtained from the MODIS sensor of the Aqua satellite. The meteorological data included 3-hour horizontal visibility data recorded in m. PM10 concentration data were obtained from the Environment Organization. Accordingly, 5 empirical equations between horizontal visibility and PM10 have been used in this case. The calculated values of particulate matter less than 10 μm in Ahvaz City, Iran, were evaluated with actual values of PM10 based on the linear regression model. Results: The study results suggested that the computational R index for empirical equations had higher accuracy in 2017 data. The coefficients of determination using D’Almeida (1986), Dayan (1986), Chung (2003), Jugder et al. (2014), and Camino et al. (2015) indicators were calculated as 0.87, 0.806, 0.745, 0.873, and 0.866, respectively. Conclusion: Jugder et al. (2014) index with R² equal to 0.548667 was the best index for estimating PM10 in this region. The study results showed that empirical equations could estimate particulate matter and dust in southwestern Iran.

Spatial and Temporal Variations in Atmospheric Ventilation Index Coupled with Particulate Matter Concentration in South Korea

The spatiotemporal variations in the atmospheric ventilation index (AVI) with the particulate matter (PM) concentrations in South Korea were investigated using a regional grid model derived from the National Center for AgroMeteorology and PM10 concentration data obtained from AirKorea and the Korea Meteorological Administration. To construct a high-resolution AVI database with 1 h time intervals and a spatial resolution of approximately 2.4 km, a medium-range prediction was performed using a regional model twice a week from December 2018 to November 2019. The resultant dataset was used to explore the seasonal patterns of the areal distribution of a novel index: Ventilation Index coupled with PM (VIP), defined by the ratio of the AVI to PM. To determine the effects of geography on the VIP, diurnal variations of the VIP were examined at three major cities in South Korea. The emphasis of the investigation was on major cities that are planned to be developed into smart cities. This study reveals the specific spatiotemporal structure of the AVI in South Korea for the first time at a high resolution and introduced the potential usefulness of the VIP. The results provide insights that could aid decision making for determining favorable locations for better air-quality cities on an annual basis and can enable the sustainable management of fine PM in and around the areas of interest.

Machine learning models to quantify the influence of PM10 aerosol concentration on global solar radiation prediction in South Africa

Solar prediction models are essential in developing countries such as South Africa as most meteorological stations are unable to consistently measure this quantity. Quantification of solar radiation is of great significance for the adoption and application of renewable energy systems. Machine learning models are infamous for their high prediction capacity and low input requirements. This study's foremost intention was to investigate the efficacy of using the most widespread machine learning techniques for solar radiation estimation across South Africa, by introducing PM10 air pollutant concentration to generalized, readily available meteorological datasets. While assessing the performance of various input parameter configurations, the techniques which were evaluated include; Artificial Neural Network (ANN), Support Vector Regression (SVR), General Regression Neural Network (GRNN), and Random Forest (RF), of which ANNs proved the most appropriate for predicting global solar radiation as indicated by the high correlation coefficients (R2 = 0.99852) and low prediction errors (RMSE = 0.22627). The results described in this work indicate that machine learning models performed excellently for hybrid models as opposed to empirical models established in South Africa. ANN models which include PM10 concentration data profoundly improved the performance of relative humidity models and reduced overall error measures for models of various input parameter configurations. In addition, a poor correlation between PM10 concentration and air temperature was observed. This work suggests that the use of generalized, hybrid ANN models to predict solar radiation across South Africa are more functional than empirical modeling and this is indicated by the high prediction accuracy and low computational effort. The suggested model suffices as an accurate prediction model which will allow for a holistic understanding of the solar capacity available across this country while encouraging the implementation and investigation of sustainable, renewable energy technologies.

MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.

Determination and evaluation of spatio-temporal trends on the example of PM10 concentration in Poland (2000−2018)

Abstract Increasing amounts of rapidly growing data are the driving force behind proposing and automating new processing, enabling the extraction of useful information from data. One of such possibilities is determining trends to consider in terms of time and space. Thus far, the analysis of these aspects has been separate and lacked automated tools. Therefore, the authors proposed, implemented, and tested a tool for analyzing spatio-temporal linear trends. The tool was tested on PM10 concentration data in the years 2000–2018. The results, presented as cartographic visualization, were then evaluated, both in terms of time and space. The proposed approach facilitates analyzing spatio-temporal trends and assessing their accuracy; it can be developed using other types of analyzed trends or considering additional factors that influence the trend by using cokriging.

Distribution Characteristics and Influencing Factors of PM2.5 in Northern-Taiwan

This paper analyzes the concentration data of PM2.5, SO2, NO2, CO and O3 recorded at 25 monitoring points in northern Taiwan in 2015. The geographical distribution and seasonal changes of PM2.5 are assessed. The geographical distribution is higher in cities than in rural areas, and higher in the west and the middle than the east. In the seasonal changes, the pollution is the most serious in spring and lightest in autumn. The author uses Pearson correlation coefficient to analyze the correlation between PM2.5 and gaseous pollutants (SO2, NO2, CO and O3). The PM2.5 concentration in Northern-Taiwan Province has a strong correlation with SO2, NO2 and CO, and a weak correlation with O3.

Evaluation of Boosted Regression Tree for the Prediction of the Maximum 24-Hour Concentration of Particulate Matter

Air pollution is a considerable health danger to the environment. The objective of this study was to assess the characteristics of air quality and predict PM10 concentrations using boosted regression trees (BRTs). The maximum daily PM10 concentration data from 2002 to 2016 were obtained from the air quality monitoring station in Kuching, Sarawak. Eighty percent of the monitoring records were used for the training and twenty percent for the validation of the models. The best iteration of the BRT model was performed by optimizing the prediction performance, while the BRT algorithm model was constructed from multiple regression models. The two main parameters that were used were the learning rate (lr) and tree complexity (tc), which were fixed at 0.01 and 5, respectively. Meanwhile, the number of trees (nt) was determined by using an independent test set (test), a 5-fold cross validation (CV) and out-of-bag (OOB) estimation. The algorithm model for the BRT produced by using the CV was the best guide to be used compared with the OOB to test the predicted PM10 concentration. The performance indicators showed that the model was adequate for the next day’s prediction (PA=0.638, R2=0.427, IA=0.749, NAE=0.267, and RMSE=28.455).

Time-Frequency Analysis of Particulate Matter (PM10) Concentration in Dry Bulk Ports Using the Hilbert-Huang Transform.

To analyze the time–frequency characteristics of the particulate matter (PM10) concentration, data series measured at dry bulk ports were used to determine the contribution of various factors during different periods to the PM10 concentration level so as to support the formulation of air quality improvement plans around port areas. In this study, the Hilbert–Huang transform (HHT) method was used to analyze the time–frequency characteristics of the PM10 concentration data series measured at three different sites at the Xinglong Port of Zhenjiang, China, over three months. The HHT method consists of two main stages, namely, empirical mode decomposition (EMD) and Hilbert spectrum analysis (HSA), where the EMD technique is used to pre-process the HSA in order to determine the intrinsic mode function (IMF) components of the raw data series. The results show that the periods of the IMF components exhibit significant differences, and the short-period IMF component provides a modest contribution to all IMF components. Using HSA technology for these IMF components, we discovered that the variations in the amplitude of the PM10 concentration over time and frequency are discrete, and the range of this variation is mainly concentrated in the low-frequency band. We inferred that long-term influencing factors determine the PM10 concentration level in the port, and short-term influencing factors determine the difference in concentration data at different sites. Therefore, when formulating PM10 emission mitigation strategies, targeted measures must be implemented according to the period of the different influencing factors. The results of this study can help guide recommendations for port authorities when formulating the optimal layout of measurement devices.

Can the New Subway Line Openings Mitigate PM10 Concentration? Evidence from Chinese Cities Based on the PSM-DID Method.

The large-scale construction of subway systems, which is viewed as one of the potential measures to mitigate traffic congestion and its resulting air pollution and health impact, is taking place in major cities throughout China. However, the literature on the impact of the new subway line openings on particulate matter with a diameter less than 10 µm (PM10) at the city level is scarce. Employing the Propensity Score Matching–Difference-in-differences method, this paper examines the effect of the new subway line openings on air quality in terms of PM10 in China, using the daily PM10 concentration data from January 2014 to December 2017. Our finding shows that the short-term treatment effect on PM10 is more controversial. Furthermore, for different time windows, the result confirms an increase in PM10 pollution during the short term, while the subway line openings improve air quality in the longer term. In addition, we find that the treatment effect results in high PM10 pollution for cities with 1–2 million people, while it improves air quality for cities with over 2 million people. Moreover, for cities with varying levels of GDP, there is evidence of a reduction in PM10 after the subway line openings. Mechanism analysis supports the conclusion that the PM10 reduction originated from substituting the subway for driving.