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Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography. SUSTAINABILITY 2022. [DOI: 10.3390/su14063388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
This study presents the assessment of the quantitative influence of atmospheric circulation on the pollutant concentration in the area of Kraków, Southern Poland, for the period 2000–2020. The research has been realized with the application of different statistical parameters, synoptic meteorology tools, the Random Forests machine learning method, and multilinear regression analyses. Another aim of the research was to evaluate the types of atmospheric circulation classification methods used in studies on air pollution dispersion and to assess the possibility of their application in air quality management, including short-term PM10 daily forecasts. During the period analyzed, a significant decreasing trend of pollutants’ concentrations and varying atmospheric circulation conditions was observed. To understand the relation between PM10 concentration and meteorological conditions and their significance, the Random Forests algorithm was applied. Observations from meteorological stations, air quality measurements and ERA-5 reanalysis were used. The meteorological database was used as an input to models that were trained to predict daily PM10 concentration and its day-to-day changes. This study made it possible to distinguish the dominant circulation types with the highest probability of occurrence of poor air quality or a significant improvement in air quality conditions. Apart from the parameters whose significant influence on air quality is well established (air temperature and wind speed at the ground and air temperature gradient), the key factor was also the gradient of relative air humidity and wind shear in the lowest troposphere. Partial dependence calculated with the use of the Random Forests model made it possible to better analyze the impact of individual meteorological parameters on the PM10 daily concentration. The analysis has shown that, for areas with a diversified topography, it is crucial to use the variability of the atmospheric circulation during the day to better forecast air quality.
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Pallarés S, Gómez ET, Martínez-Poveda Á, Jordán MM. Distribution Levels of Particulate Matter Fractions (<2.5 µm, 2.5-10 µm and >10 µm) at Seven Primary Schools in a European Ceramic Cluster. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18094922. [PMID: 34063092 PMCID: PMC8124735 DOI: 10.3390/ijerph18094922] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/21/2021] [Accepted: 04/27/2021] [Indexed: 11/16/2022]
Abstract
This study addresses the concentration of particulate matter and their size using a statistical analysis of data obtained inside seven schools located in the towns of Castellón (S1, S2, and S3), Alcora (S4, S5, and S6) and Lucena (S7) in northeast Spain. Samples were taken for five to eight hours, depending on school hours, to obtain a monthly sample for each school. The main goal of this study is to assess the differences depending on the type of location and the sampling point to be able to design corrective measures that improve the habitability and safety of the teaching spaces analyzed. The lowest concentrations of fine particulate matter, less than 2.5 µm, were registered at the rural location. The values of these particles found in industrial and urban locations were not substantially different. In the case of particulate matter between 2.5 and 10 µm, significant differences were observed between the three types of locations. The lowest concentrations of particles larger than 10 µm were registered at the rural location, and the highest concentrations were found at the industrial locations. Among the urban stations, the particle concentration of this fraction in station S2 was significantly higher than that in stations S1 and S3, which had similar concentrations. These values are also similar to those registered at school S6, which is at an industrial location. The resuspension of particles from both indoor sources as well as those transported from the outside is an important factor in the concentrations of particles inside classrooms.
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Affiliation(s)
| | - Eva Trinidad Gómez
- Department of Agricultural and Environmental Sciences, Jaume I University, Campus Riu Sec s/n, 12071 Castellón, Spain;
| | - África Martínez-Poveda
- Department of Agricultural Economics, Cartographic Engineering, Graphic Expression in Engineering, Miguel Hernández University of Elche, 03312 Orihuela (Alicante), Spain;
| | - Manuel Miguel Jordán
- Department of Agrochemistry and Environment, Miguel Hernández University of Elche, 03202 Elche (Alicante), Spain
- Correspondence: ; Tel.: +34-966658896
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Gayen A, Haque SM, Mishra SV. COVID-19 induced lockdown and decreasing particulate matter (PM10): An empirical investigation of an Asian megacity. URBAN CLIMATE 2021; 36:100786. [PMID: 33552884 PMCID: PMC7846237 DOI: 10.1016/j.uclim.2021.100786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 07/28/2020] [Accepted: 01/22/2021] [Indexed: 05/09/2023]
Abstract
The air quality in the cities of developing countries is deteriorating with the proliferation of anthropogenic activities that add pollutant matters in the lower part of the troposphere. Particulate matter with an aerodynamic diameter lower than 10 μm (PM10) is considered one of the direct indicators of air quality in an urban area as it brings health morbidities. The article empirically investigates the role COVID-19 related lockdown has played in bringing down pollution level (PM10) in the megacity of Kolkata. It does so by taking account of PM10 level in three stages - pre, presage and complete-lockdown timelines. The extracted results show a significant declining trend (about 77% vis-a-vis the pre-lockdown period) with 95% of the geographical area under 100 μm/m3 and a strong fit with the station-based records. The feasibility and robustness showed by the remotely sensed data along with other earth observatory information for larger-scale pollution prevalence make its adoption imperative. Simultaneously, it becomes urgent in times of lockdown when the physical mobility of maintenance and research staff to stations is significantly curtailed. The work contributes to study on PM10 by its ability to replicate in examining cities of both the global north and global south.
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Affiliation(s)
- Amiya Gayen
- Department of Geography, The University of Calcutta, 35 B. C. Road, Kolkata 700 019, India
| | - Sk Mafizul Haque
- Department of Geography, The University of Calcutta, 35 B. C. Road, Kolkata 700 019, India
| | - Swasti Vardhan Mishra
- Department of Geography, The University of Calcutta, 35 B. C. Road, Kolkata 700 019, India
- Department of Geography, Amity Institute of Social Sciences, Amity University Kolkata, Rajarhat, Newtown, Kolkata 700135, West Bengal, India
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Lee S, Kim M, Kim SY, Lee DW, Lee H, Kim J, Le S, Liu Y. Assessment of long-range transboundary aerosols in Seoul, South Korea from Geostationary Ocean Color Imager (GOCI) and ground-based observations. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 269:115924. [PMID: 33221083 DOI: 10.1016/j.envpol.2020.115924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/30/2020] [Accepted: 10/22/2020] [Indexed: 06/11/2023]
Abstract
To better understand air quality issues in South Korea, it is essential to identify the main contributors of air pollution and to quantify the effects of transboundary transport. In this study, geostationary satellite measurements were used to assess the effects of aerosol transport on air quality in South Korea. This study proposes a method to define the long-range transport (LRT) of aerosols into the Korean Peninsula using remote sensing obervations and back-trajectories and estimates the LRT effects on air quality in Seoul using in-situ particulate matter (PM) measurements. Aerosol optical depths (AODs) are obtained from the Geostationary Ocean Color Imager (GOCI), and the back-trajectories are from the National Ocean and Atmospheric Administration (NOAA) HYbrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. For LRT events, satellite observations showed high AOD plumes over the Yellow Sea, a pathway between Eastern China and South Korea, and the movements of aerosol plumes transported to South Korea were also detected. PM2.5 concentrations, PM10 concentrations, and AOD during LRT increased by 52%, 49%, and 81%, respectively, relative to their average values for 2015-2018. To quantitatively characterize the LRT of aerosols, the effects of LRT on PM2.5 concentrations were estimated for each PM concentration category. The contribution of LRT to PM2.5 concentrations was estimated to be 33% during 2015-2018. When high concentrations of PM2.5 were observed in Seoul, they were likely to be associated with LRT events.
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Affiliation(s)
- Seoyoung Lee
- Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, South Korea
| | - Minseok Kim
- Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, South Korea
| | - Seung-Yeon Kim
- Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon, 22689, South Korea
| | - Dong-Won Lee
- Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon, 22689, South Korea
| | - Hanlim Lee
- Division of Earth Environmental System Science, Major of Spatial Information Engineering, Pukyong National University, Busan, 48513, South Korea
| | - Jhoon Kim
- Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, South Korea.
| | - Sophia Le
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 20218, USA
| | - Yang Liu
- Emory University, Rollins School of Public Health, Atlanta, GA, 30322, USA
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Reliability Validation of a Low-Cost Particulate Matter IoT Sensor in Indoor and Outdoor Environments Using a Reference Sampler. SUSTAINABILITY 2019. [DOI: 10.3390/su11247220] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A suitable and quick determination of air quality allows the population to be alerted with respect to high concentrations of pollutants. Recent advances in computer science have led to the development of a high number of low-cost sensors, improving the spatial and temporal resolution of air quality data while increasing the effectiveness of risk assessment. The main objective of this work is to perform a validation of a particulate matter (PM) sensor (HM-3301) in indoor and outdoor environments to study PM2.5 and PM10 concentrations. To date, this sensor has not been evaluated in real-world situations, and its data quality has not been documented. Here, the HM-3301 sensor is integrated into an Internet of things (IoT) platform to establish a permanent Internet connection. The validation is carried out using a reference sampler (LVS3 of Derenda) according to EN12341:2014. It is focused on statistical insight, and environmental conditions are not considered in this study. The ordinary Linear Model, the Generalized Linear Model, Locally Estimated Scatterplot Smoothing, and the Generalized Additive Model have been proposed to compare and contrast the outcomes. The low-cost sensor is highly correlated with the reference measure ( R 2 greater than 0.70), especially for PM2.5, with a very high accuracy value. In addition, there is a positive relationship between the two measurements, which can be appropriately fitted through the Locally Estimated Scatterplot Smoothing model.
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Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9142806] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM10. Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM10 in open-pit mines. Therefore, the prediction and control of PM10 concentration in the production process are necessary for environmental and health protection. In this study, PM10 concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM10 concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as k-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM10 concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE) and determination coefficient (R2) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models. The PSO-SVR models (i.e., PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF) had higher performance levels than the other models (i.e., RF, CART, and KNN) with an RMSE of 0.040, 0.042, and 0.043; and R2 of 0.954, 0.948, and 0.946; for the PSO-SVR-L, PSO-SVR-P, and PSO-SVR-RBF models, respectively. Of these PSO-SVR models, the PSO-SVR-L model was the most dominant model with an RMSE of 0.040 and R2 of 0.954. The remaining three benchmark models (i.e., RF, CART, and KNN) yielded a more unsatisfactory performance with an RMSE of 0.060, 0.052, and 0.067; and R2 of 0.894, 0.924, and 0.867, for the RF, CART, and KNN models, respectively. Furthermore, the findings of this study demonstrated that the density of rock mass, moisture content, and the penetration rate of the drill were essential parameters on the PM10 concentration caused by drilling operations in open-pit mines.
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Liu T, Tian Y, Xue Q, Wei Z, Qian Y, Feng Y. An advanced three-way factor analysis model (SDABB model) for size-resolved PM source apportionment constrained by size distribution of chemical species in source profiles. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2018; 242:1606-1615. [PMID: 30064874 DOI: 10.1016/j.envpol.2018.07.118] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/18/2018] [Accepted: 07/24/2018] [Indexed: 06/08/2023]
Abstract
Source samples including crustal dust, cement dust, coal combustion were sampled and ambient samples of PM2.5 and PM10 were synchronously collected in Hefei from April to December 2014. The size distributions of the markers in the measured source profiles were incorporated into ME-2 solution to develop a new method, called the SDABB model (an advanced ABB three-way factor analysis model incorporating size distribution information). The performance of this model was investigated using three-way synthetic and ambient dataset. For the synthetic tests, the size distributions of markers estimated by the SDABB model were more consistent with true condition. The AAEs between estimated and observed contributions of the SDABB ranged from 15.2% to 29.0% for PM10 and 19.9%-31.6% for PM2.5, which is lower than those of PMF2. For the ambient PM, six source categories were identified by SDABB for both sizes, although the profiles were different. The source contributions were sulphate (33.33% and 24.53%), nitrate and SOC (22.33% and 18.16%), coal combustion (19.01% and 18.23%), vehicular exhaust (12.99% and 12.07%), crustal dust (10.69% and 19.40%) and cement dust (1.65% and 5.39%) for PM2.5 and PM10 respectively. In addition, the estimated ratios of Al, Si, Ti and Fe in CRD were 0.76, 0.84, 1.10 and 0.85; those of Al and Si in CC were 0.42 and 0.66; Ca and Si in CD were 0.95 and 1.10; NO3- and NH4+ in nitrate were 1.11 and 1.01; and SO42- and NH4+ in sulphate were 0.96 and 1.16. These modeled ratios were consistent with the measured ratios. The size distribution of contributions also came close to reality. Thus, the advanced SDABB three-way model can better capture the characteristics of sources between sizes by effectively incorporating the size distributions of the markers as physical constraints.
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Affiliation(s)
- Tong Liu
- The State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Yingze Tian
- The State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China.
| | - Qianqian Xue
- The State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
| | - Zhen Wei
- Anhui Environment Monitoring Center, Hefei, 230000, China
| | - Yong Qian
- Hefei Environment Monitoring Center, Hefei, 230000, China
| | - Yinchang Feng
- The State Environmental Protection Key Laboratory of Urban Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
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