A multi-objective assessment of an air quality monitoring network using environmental, economic, and social indicators and GIS-based models

An optimized development of urban air quality monitoring network design based on particulate matters

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500 m * 500 m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

A novel assessment framework for improving air quality monitoring network layout

Redundant stations in the air quality monitoring network (AQMN), not only cause high maintenance and operation costs, but also affect the performance of air quality assessment. This study presents a novel framework for identifying the redundant stations and selecting the corresponding alternatives in AQMN. The framework composes three main steps. Firstly, we identify the redundant stations by correlation analysis and stepwise regression methods. Secondly, we determine the corresponding alternative stations by cluster analysis and correspondence analysis methods. Finally, the final optimization results are verified by the support vector regression. We perform empirical evaluations of the framework using Shanghai's AQMN. The results show that Xuhui, Zhangjiang, Shiwuchang, and Pudong New Area are four redundant pollution monitoring stations. Alternatives for each type of pollutant for these redundant stations are proposed and the adjusted layout of AQMN is verified with historical data. The framework proposed in this study can effectively improve the layout of AQMN, which could be applied to other cities or regions to improve the integrity of pollution information and reduce the monitoring costs.Implications: In this study, we set up a comprehensive framework. A case study proves that the framework we proposed can help countries identify redundant stations, so as to reduce the monitoring costs, improve the monitoring efficiency, and provide technical support for governments to implement accurate air quality control measures.Four particularly important aspects were highlighted in this work: (i) A new framework was constructed that combined regression and prediction for the first time to analyze and validate pollutant data; (ii) The framework used Stepwise Regression to improve previous methods for identifying redundant monitoring stations, effectively improving identification efficiency; (iii) The framework used Support Vector Regression to make predictions to verify the final results of the optimized layout, which was ignored in previous studies. (iv) This framework can be applied to any city or region, which has important practical significance for improving the comprehensiveness and accuracy of pollution monitoring in various cities.

A methodological framework for improving air quality monitoring network layout. Applications to environment management

This work aims to provide a methodology framework which allows to improve the performance and efficiency of an air quality monitoring network (AQMN). It requires to be constituted by a minimum and reliable number of measurement sites. Nevertheless, the AQMN efficiency should be assessed over time, as a consequence of the possible emergence of new emission sources of air pollutants, which could lead to variations on their spatial distribution within the target area. PM₁₀ particles data monitored by the Community of Madrid's (Spain) AQMN between 2008 and 2017 were used to develop a methodology to optimize the AQMN performance. The annual spatial distribution of average PM₁₀ levels over the studied period monitored by all current stations vs those more representative was provided by a geographic information system (GIS), and the percentage of similarity between both postulates was quantified using simple linear regression (> 95%). As one innovative tool of this study, the practical application of the proposed methodology was validated using PM₁₀ particles data measured by AQMN during 2007 and 2018, reaching a similitude degree higher than 95%. The influence of temporal variation on the proposed methodological framework was around 20%. The proposed methodology sets criteria for identifying non-redundant stations within AQMN, it is also able to appropriately assess the representativeness of fixed monitoring sites within an AQMN and it complements the guidelines set by European legislation on air pollutants monitoring at fixed stations, which could help to tackle efforts to improve the air quality management.

A multi-pollutant methodology to locate a single air quality monitoring station in small and medium-size urban areas

Air quality management is underpinned by continuous measurements of concentrations of target air pollutants in monitoring stations. Although many approaches for optimizing the number and location of air quality monitoring stations are described in the literature, these are usually focused on dense networks. However, there are small and medium-size urban areas that only require one monitoring station but also suffer from severe air pollution. Given that target pollutants are usually measured at the same sampling points; it is necessary to develop a methodology to determine the optimal location of the single station. In this paper, such a methodology is proposed based on maximizing an objective function, that balances between different pollutants measured in the network. The methodology is applied to a set of data available for the city of Cartagena, in southeast Spain. A sensitivity analysis reveals that 2 small areas of the studied city account for 80% of the optimal potential locations, which makes them ideal candidates for setting up the monitoring station. The methodology is easy to implement, robust and supports the decision-making process regarding the siting of fixed sampling sites.

Layout Planning of Highway Transportation Environment Monitoring Network: The Case of Xinjiang, China

Environmental monitoring is an important tool for environmental protection supervision and management. Environmental monitoring can help us effectively understand and master the degree of environmental pollution, and provide data support for putting forward environmental protection measures. Scientific layout and reasonable level of environmental monitoring network design is an essential cornerstone for environmental monitoring, and a significant measure to promote the industry and green sustainable development. This paper systematically analyzed its requirements of monitoring stations in the highway traffic environment monitoring network. First of all, the paper analyzed the influencing factors of regional monitoring stations in the Xinjiang transportation environment monitoring network by referring to the idea of planning the distribution points of the national transportation environment monitoring network, and determines the weight of them by using the analytic hierarchy process (AHP), which lays a foundation for the subsequent selection and determination of environmental monitoring stations. Secondly, the advantage order of ecological monitoring objects’ importance degree was synthetically sorted by the fuzzy comprehensive evaluation method. Finally, the ranking results of the environmental monitoring objects were integrated to determine the number of traffic environmental monitoring stations that need to be built, and the layout of the highway traffic environment monitoring network in Xinjiang was proposed.

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

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.

Determination of the physical domain for air quality monitoring stations using the ANP-OWA method in GIS

Air pollution is a major concern in some megacities of Iran. Specific cities in the country have reached an extremely harmful level of air pollution which poses a serious risk to the daily lives of Iranians. According to news reports, the air quality index of the city of Tehran hovers around 159, which is more than three times the World Health Organization's advised maximum. For the purpose of air pollution abatement, it is necessary to precisely know the air pollution distribution in the area. In order to obtain this figure, it is necessary to properly locate the city's air quality monitoring stations that measure the spatial pollutant distribution. According to various reports, the city must have at least 56 air quality monitoring stations to properly measure Tehran's air quality. However, there are currently only 20 stations within the city. Thus, the main purpose of this study was to identify the most sufficient areas for deploying new air quality monitoring stations. This study provided an integration of hybrid multi-criteria decision-making (MCDM) theories and geographical information system (GIS) processes in order to determine suitable areas to establish air quality monitoring stations. Unlike traditional models, the proposed MCDM method, ANP-OWA, is an efficient decision analysis which considers dependencies between criteria and defines different scenarios between pessimistic and optimistic conditions for decision makers. This method was applied to several parameters such as point, area, and line sources; population density; sensitive receptors; distance from current air quality stations; prediction error; and spatial distribution of CO, NO₂, SO₂, and PM₁₀ pollutants. The output results specified several suitable locations to establish air pollution monitoring stations within Tehran Province. The stability and reliability of the output results were evaluated with a robust sensitivity analysis method. Moreover, the results demonstrated that the proposed method can produce stable results. Obtaining knowledge regarding population density, distance from current air quality stations, and spatial distribution of CO pollutant criteria is essential when selecting locations for air quality monitoring stations.

Optimal Design of Air Quality Monitoring Network for Pollution Detection and Source Identification in Industrial Parks

Dense air quality monitoring network (AQMN) is one of main ways to surveil industrial air pollution. This paper is concerned with the design of a dense AQMN for H2S for a chemical industrial park in Shanghai, China. An indicator (Surveillance Efficiency, SE) for the long-term performance of AQMN was constructed by averaging pollution detection efficiency (rd) and source identification efficiency (rb). A ranking method was developed by combing Gaussian puff model and Source area analysis for improving calculation efficiency. Candidate combinations with highest score were given priority in the selection of next site. Two existing monitors were suggested to relocate to the west and southwest of this park. SE of optimized AQMN increased quickly with monitor number, and then the growth trend started to flatten when the number reached about 60. The highest SE occurred when the number reached 110. Optimal schemes of AQMNs were suggested which can achieve about 98% of the highest SE, while using only about 60 monitors. Finally, the reason why the highest SE is less than 1 and the variation characteristics of rd and rb were discussed. Overall, the proposed method is an effective tool for designing AQMN with optimal SE in industrial parks.

Understanding the Patterns and Drivers of Air Pollution on Multiple Time Scales: The Case of Northern China

China's rapid economic growth during the past three decades has resulted in a number of environmental problems, including the deterioration of air quality. It is necessary to better understand how the spatial pattern of air pollutants varies with time scales and what drive these changes. To address these questions, this study focused on one of the most heavily air-polluted areas in North China. We first quantified the spatial pattern of air pollution, and then systematically examined the relationships of air pollution to several socioeconomic and climatic factors using the constraint line method, correlation analysis, and stepwise regression on decadal, annual, and seasonal scales. Our results indicate that PM_2.5 was the dominant air pollutant in the Beijing-Tianjin-Hebei region, while PM_2.5 and PM₁₀ were both important pollutants in the Agro-pastoral Transitional Zone (APTZ) region. Our statistical analyses suggest that energy consumption and gross domestic product (GDP) in the industry were the most important factors for air pollution on the decadal scale, but the impacts of climatic factors could also be significant. On the annual and seasonal scales, high wind speed, low relative humidity, and long sunshine duration constrained PM_2.5 accumulation; low wind speed and high relative humidity constrained PM₁₀ accumulation; and short sunshine duration and high wind speed constrained O₃ accumulation. Our study showed that analyses on multiple temporal scales are not only necessary to determine key drivers of air pollution, but also insightful for understanding the spatial patterns of air pollution, which was important for urban planning and air pollution control.

Characterization and Local Emission Sources for Ammonia in an Urban Environment

Ammonia levels were evaluated in the urban environment of Madrid City, Spain. A total of 110 samplers were distributed throughout the city. Vehicle traffic density, garbage containers and sewers were identified as local emission sources of ammonia. The average ammonia concentrations were 4.66 ± 2.14 µg/m³ (0.39-11.23 µg/m³ range) in the winter and 5.30 ± 1.81 µg/m³ (2.33-11.08 µg/m³ range) in the summer. Spatial and seasonal variations of ammonia levels were evaluated. Hotspots were located in the south and center of Madrid City in both winter and summer seasons, with lower ammonia concentrations located in the north (winter) and in the west and east (summer). The number of representative points that were needed to establish a reliable air quality monitoring network for ammonia was determined using a combined clustering and kriging approach. The results indicated that 40 samplers were sufficient to provide a reliable estimate for Madrid City.

Systematic review of community health impacts of mountaintop removal mining

BACKGROUND

The objective of this evaluation is to understand the human health impacts of mountaintop removal (MTR) mining, the major method of coal mining in and around Central Appalachia. MTR mining impacts the air, water, and soil and raises concerns about potential adverse health effects in neighboring communities; exposures associated with MTR mining include particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), metals, hydrogen sulfide, and other recognized harmful substances.

METHODS

A systematic review was conducted of published studies of MTR mining and community health, occupational studies of MTR mining, and any available animal and in vitro experimental studies investigating the effects of exposures to MTR-mining-related chemical mixtures. Six databases (Embase, PsycINFO, PubMed, Scopus, Toxline, and Web of Science) were searched with customized terms, and no restrictions on publication year or language, through October 27, 2016. The eligibility criteria included all human population studies and animal models of human health, direct and indirect measures of MTR-mining exposure, any health-related effect or change in physiological response, and any study design type. Risk of bias was assessed for observational and experimental studies using an approach developed by the National Toxicology Program (NTP) Office of Health Assessment and Translation (OHAT). To provide context for these health effects, a summary of the exposure literature is included that focuses on describing findings for outdoor air, indoor air, and drinking water.

RESULTS

From a literature search capturing 3088 studies, 33 human studies (29 community, four occupational), four experimental studies (two in rat, one in vitro and in mice, one in C. elegans), and 58 MTR mining exposure studies were identified. A number of health findings were reported in observational human studies, including cardiopulmonary effects, mortality, and birth defects. However, concerns for risk of bias were identified, especially with respect to exposure characterization, accounting for confounding variables (such as socioeconomic status), and methods used to assess health outcomes. Typically, exposure was assessed by proximity of residence or hospital to coal mining or production level at the county level. In addition, assessing the consistency of findings was challenging because separate publications likely included overlapping case and comparison groups. For example, 11 studies of mortality were conducted with most reporting higher rates associated with coal mining, but many of these relied on the same national datasets and were unable to consider individual-level contributors to mortality such as poor socioeconomic status or smoking. Two studies of adult rats reported impaired microvascular and cardiac mitochondrial function after intratracheal exposure to PM from MTR-mining sites. Exposures associated with MTR mining included reports of PM levels that sometimes exceeded Environmental Protection Agency (EPA) standards; higher levels of dust, trace metals, hydrogen sulfide gas; and a report of increased public drinking water violations.

DISCUSSION

This systematic review could not reach conclusions on community health effects of MTR mining because of the strong potential for bias in the current body of human literature. Improved characterization of exposures by future community health studies and further study of the effects of MTR mining chemical mixtures in experimental models will be critical to determining health risks of MTR mining to communities. Without such work, uncertainty will remain regarding the impact of these practices on the health of the people who breathe the air and drink the water affected by MTR mining.

Spatial patterns of air pollutants and social groups: a distributive environmental justice study in the phoenix metropolitan region of USA

Quantifying spatial distribution patterns of air pollutants is imperative to understand environmental justice issues. Here we present a landscape-based hierarchical approach in which air pollution variables are regressed against population demographics on multiple spatiotemporal scales. Using this approach, we investigated the potential problem of distributive environmental justice in the Phoenix metropolitan region, focusing on ambient ozone and particulate matter. Pollution surfaces (maps) are evaluated against the demographics of class, age, race (African American, Native American), and ethnicity (Hispanic). A hierarchical multiple regression method is used to detect distributive environmental justice relationships. Our results show that significant relationships exist between the dependent and independent variables, signifying possible environmental inequity. Although changing spatiotemporal scales only altered the overall direction of these relationships in a few instances, it did cause the relationship to become nonsignificant in many cases. Several consistent patterns emerged: people aged 17 and under were significant predictors for ambient ozone and particulate matter, but people 65 and older were only predictors for ambient particulate matter. African Americans were strong predictors for ambient particulate matter, while Native Americans were strong predictors for ambient ozone. Hispanics had a strong negative correlation with ambient ozone, but a less consistent positive relationship with ambient particulate matter. Given the legacy conditions endured by minority racial and ethnic groups, and the relative lack of mobility of all the groups, our findings suggest the existence of environmental inequities in the Phoenix metropolitan region. The methodology developed in this study is generalizable with other pollutants to provide a multi-scaled perspective of environmental justice issues.

Assessment of regional air quality by a concentration-dependent Pollution Permeation Index

Although air quality monitoring networks have been greatly improved, interpreting their expanding data in both simple and efficient ways remains challenging. Therefore, needed are new analytical methods. We developed such a method based on the comparison of pollutant concentrations between target and circum areas (circum comparison for short), and tested its applications by assessing the air pollution in Jing-Jin-Ji, Yangtze River Delta, Pearl River Delta and Cheng-Yu, China during 2015. We found the circum comparison can instantly judge whether a city is a pollution permeation donor or a pollution permeation receptor by a Pollution Permeation Index (PPI). Furthermore, a PPI-related estimated concentration (original concentration plus halved average concentration difference) can be used to identify some overestimations and underestimations. Besides, it can help explain pollution process (e.g., Beijing’s PM_2.5 maybe largely promoted by non-local SO₂) though not aiming at it. Moreover, it is applicable to any region, easy-to-handle, and able to boost more new analytical methods. These advantages, despite its disadvantages in considering the whole process jointly influenced by complex physical and chemical factors, demonstrate that the PPI based circum comparison can be efficiently used in assessing air pollution by yielding instructive results, without the absolute need for complex operations.

Selection of Atmospheric Environmental Monitoring Sites based on Geographic Parameters Extraction of GIS and Fuzzy Matter-Element Analysis

To effectively monitor the atmospheric quality of small-scale areas, it is necessary to optimize the locations of the monitoring sites. This study combined geographic parameters extraction by GIS with fuzzy matter-element analysis. Geographic coordinates were extracted by GIS and transformed into rectangular coordinates. These coordinates were input into the Gaussian plume model to calculate the pollutant concentration at each site. Fuzzy matter-element analysis, which is used to solve incompatible problems, was used to select the locations of sites. The matter element matrices were established according to the concentration parameters. The comprehensive correlation functions KA (xj) and KB (xj), which reflect the degree of correlation among monitoring indices, were solved for each site, and a scatter diagram of the sites was drawn to determine the final positions of the sites based on the functions. The sites could be classified and ultimately selected by the scatter diagram. An actual case was tested, and the results showed that 5 positions can be used for monitoring, and the locations conformed to the technical standard. In the results of this paper, the hierarchical clustering method was used to improve the methods. The sites were classified into 5 types, and 7 locations were selected. Five of the 7 locations were completely identical to the sites determined by fuzzy matter-element analysis. The selections according to these two methods are similar, and these methods can be used in combination. In contrast to traditional methods, this study monitors the isolated point pollutant source within a small range, which can reduce the cost of monitoring.