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A Novel Framework for Forecasting, Evaluation and Early-Warning for the Influence of PM10 on Public Health. ATMOSPHERE 2021. [DOI: 10.3390/atmos12081020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
PM2.5 has attracted widespread attention since the public has become aware of it, while attention to PM10 has started to wane. Considering the significance of PM10, this study takes PM10 as the research object and raises a significant question: when will the influence of PM10 on public health end? To answer the abovementioned question, two promising research areas, i.e., air pollution forecasting and health effects analysis, are employed, and a novel hybrid framework is developed in this study, which consists of one effective model and one evaluation model. More specifically, this study first introduces one advanced optimization algorithm and cycle prediction theory into the grey forecasting model to develop an effective model for multistep forecasting of PM10, which can achieve reasonable forecasting of PM10. Then, an evaluation model is designed to evaluate the health effects and economic losses caused by PM10. Considering the significance of providing the future impact of PM10 on public health, we extend our forecasting results to evaluate future changes in health effects and economic losses based on our proposed health economic losses evaluation model. Accordingly, policymakers can adjust current air pollution prevention plans and formulate new plans according to the results of forecasting, evaluation and early-warning. Empirical research shows that the developed framework is applicable in China and may become a promising technique to enrich the current research and meet the requirements of air quality management and haze governance.
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Tomassetti L, Torre M, Tratzi P, Paolini V, Rizza V, Segreto M, Petracchini F. Evaluation of air quality and mobility policies in 14 large Italian cities from 2006 to 2016. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART A, TOXIC/HAZARDOUS SUBSTANCES & ENVIRONMENTAL ENGINEERING 2020; 55:886-902. [PMID: 32295485 DOI: 10.1080/10934529.2020.1752070] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 03/24/2020] [Accepted: 03/24/2020] [Indexed: 06/11/2023]
Abstract
Despite a connection between road traffic and air pollution has been clearly identified, few integrated analyses on air quality and mobility policies are currently available worldwide. Aim of this work is to build a framework for the evaluation of the impact of mobility policies on air quality in 14 major Italian cities, on a long-time scale (2006-2016). Air quality data was collected from all PM10, PM2.5, and NO2 monitoring stations, installed in compliance with the European directive 2008/50/EC. A large dataset was collected, including public transport, environmental, and fuel categorization of the private vehicular fleet, low emissions zones and modal split. In the analyzed time period, both public transportation use and private motorization decreased. Considering the environmental classification standards of vehicles, Northern cities are more readily switching to newer and less polluting cars (e.g. Euro 5 and Euro 6). There has been a general reduction in PM and NO2 concentration. Nevertheless, exceedances are still above the targeted limit value, mainly in some Northern cities who made major investments in sustainable and shared mobility: this highlights a strong influence of climatic conditions and other sources. Dramatic variations in the CO/NO ratio were observed in Turin, while smaller changes are observed in Milan, Rome and Palermo.
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Affiliation(s)
- Laura Tomassetti
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
| | - Marco Torre
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
| | - Patrizio Tratzi
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
| | - Valerio Paolini
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
| | - Valeria Rizza
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
| | - Marco Segreto
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
| | - Francesco Petracchini
- National Research Council of Italy, Institute of Atmospheric Pollution Research, Monterotondo, Rome, Italy
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Assessment of Remote Sensing Data to Model PM10 Estimation in Cities with a Low Number of Air Quality Stations: A Case of Study in Quito, Ecuador. ENVIRONMENTS 2019. [DOI: 10.3390/environments6070085] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The monitoring of air pollutant concentration within cities is crucial for environment management and public health policies in order to promote sustainable cities. In this study, we present an approach to estimate the concentration of particulate matter of less than 10 µm diameter (PM10) using an empirical land use regression (LUR) model and considering different remote sensing data as the input. The study area is Quito, the capital of Ecuador, and the data were collected between 2013 and 2017. The model predictors are the surface reflectance bands (visible and infrared) of Landsat-7 ETM+, Landsat-8 OLI/TIRS, and Aqua-Terra/MODIS sensors and some environmental indexes (normalized difference vegetation index—NDVI; normalized difference soil index—NDSI, soil-adjusted vegetation index—SAVI; normalized difference water index—NDWI; and land surface temperature (LST)). The dependent variable is PM10 ground measurements. Furthermore, this study also aims to compare three different sources of remote sensing data (Landsat-7 ETM+, Landsat-8 OLI, and Aqua-Terra/MODIS) to estimate the PM10 concentration, and three different predictive techniques (stepwise regression, partial least square regression, and artificial neuronal network (ANN)) to build the model. The models obtained are able to estimate PM10 in regions where air data acquisition is limited or even does not exist. The best model is the one built with an ANN, where the coefficient of determination (R2 = 0.68) is the highest and the root-mean-square error (RMSE = 6.22) is the lowest among all the models. Thus, the selected model allows the generation of PM10 concentration maps from public remote sensing data, constituting an alternative over other techniques to estimate pollutants, especially when few air quality ground stations are available.
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Jain S, Sharma SK, Choudhary N, Masiwal R, Saxena M, Sharma A, Mandal TK, Gupta A, Gupta NC, Sharma C. Chemical characteristics and source apportionment of PM 2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2017; 24:14637-14656. [PMID: 28455568 DOI: 10.1007/s11356-017-8925-5] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Accepted: 03/23/2017] [Indexed: 05/10/2023]
Abstract
The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM2.5 was recorded as 121.9 ± 93.2 μg m-3 (range 25.1-429.8 μg m-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM2.5. Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM2.5 from the north-west India and Pakistan were mostly pertinent.
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Affiliation(s)
- Srishti Jain
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Physical Laboratory campus, New Delhi, 110 012, India
| | - Sudhir Kumar Sharma
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India.
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Physical Laboratory campus, New Delhi, 110 012, India.
| | - Nikki Choudhary
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- University School of Environment Management, GGS Indraprastha University, New Delhi, 110 017, India
| | - Renu Masiwal
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- University School of Environment Management, GGS Indraprastha University, New Delhi, 110 017, India
| | - Mohit Saxena
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
| | - Ashima Sharma
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Physical Laboratory campus, New Delhi, 110 012, India
| | - Tuhin Kumar Mandal
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Physical Laboratory campus, New Delhi, 110 012, India
| | - Anshu Gupta
- University School of Environment Management, GGS Indraprastha University, New Delhi, 110 017, India
| | - Naresh Chandra Gupta
- University School of Environment Management, GGS Indraprastha University, New Delhi, 110 017, India
| | - Chhemendra Sharma
- Environmental Sciences and Biomedical Metrology Division, CSIR-National Physical Laboratory, Dr. K. S. Krishnan Road, New Delhi, 110 012, India
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Galindo N, Varea M, Gil-Moltó J, Yubero E. BTX in urban areas of eastern Spain: a focus on time variations and sources. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2016; 23:18267-18276. [PMID: 27272924 DOI: 10.1007/s11356-016-7019-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 06/01/2016] [Indexed: 06/06/2023]
Abstract
Seasonal and daily cycles of BTX were studied in a non-industrialized city (Alicante) and an urban area near an oil refinery plant (Castellón) in order to evaluate the influence of different sources on time variations. Lower levels were observed in summer than in winter at both locations due to higher dispersion conditions and photochemical removal of BTX during the summer season. Daily patterns showed seasonal differences and were controlled by traffic emissions and the evolution of the mixing layer height, with no influence of the petroleum refinery plant in the city of Castellón. The results of the conditional bivariate probability function suggest that the influence of this source on BTX concentrations was limited to point impacts. At both sites, benzene exhibited a different behavior from toluene and xylenes, most likely due to its significantly lower chemical reactivity.
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Affiliation(s)
- Nuria Galindo
- Atmospheric Pollution Laboratory (LCA), Department of Applied Physics, Miguel Hernández University, Avenida de la Universidad S/N, 03202, Elche, Spain.
| | - Montse Varea
- Atmospheric Pollution Laboratory (LCA), Department of Applied Physics, Miguel Hernández University, Avenida de la Universidad S/N, 03202, Elche, Spain
| | - Juan Gil-Moltó
- Atmospheric Pollution Laboratory (LCA), Department of Applied Physics, Miguel Hernández University, Avenida de la Universidad S/N, 03202, Elche, Spain
| | - Eduardo Yubero
- Atmospheric Pollution Laboratory (LCA), Department of Applied Physics, Miguel Hernández University, Avenida de la Universidad S/N, 03202, Elche, Spain
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Wang J, Liu R, Wang H, Yu W, Xu F, Shen Z. Identification and apportionment of hazardous elements in the sediments in the Yangtze River estuary. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2015; 22:20215-20225. [PMID: 26511260 DOI: 10.1007/s11356-015-5642-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2014] [Accepted: 10/19/2015] [Indexed: 06/05/2023]
Abstract
In this study, positive matrix factorization (PMF) and principal components analysis (PCA) were combined to identify and apportion pollution-based sources of hazardous elements in the surface sediments in the Yangtze River estuary (YRE). Source identification analysis indicated that PC1, including Al, Fe, Mn, Cr, Ni, As, Cu, and Zn, can be defined as a sewage component; PC2, including Pb and Sb, can be considered as an atmospheric deposition component; and PC3, containing Cd and Hg, can be considered as an agricultural nonpoint component. To better identify the sources and quantitatively apportion the concentrations to their sources, eight sources were identified with PMF: agricultural/industrial sewage mixed (18.6 %), mining wastewater (15.9 %), agricultural fertilizer (14.5 %), atmospheric deposition (12.8 %), agricultural nonpoint (10.6 %), industrial wastewater (9.8 %), marine activity (9.0 %), and nickel plating industry (8.8 %). Overall, the hazardous element content seems to be more connected to anthropogenic activity instead of natural sources. The PCA results laid the foundation for the PMF analysis by providing a general classification of sources. PMF resolves more factors with a higher explained variance than PCA; PMF provided both the internal analysis and the quantitative analysis. The combination of the two methods can provide more reasonable and reliable results.
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Affiliation(s)
- Jiawei Wang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China
| | - Ruimin Liu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China.
| | - Haotian Wang
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China
| | - Wenwen Yu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China
| | - Fei Xu
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China
| | - Zhenyao Shen
- State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing, 100875, China
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