scholarly journals Towards Multi-Scale Space-Time Characteristics of Air Quality and Population Exposure Risk

2021 ◽  
Vol 14 (1) ◽  
pp. 96
Author(s):  
Xiao Xiao ◽  
Xiao Xie ◽  
Bingyu Zhao ◽  
Jingzhong Li ◽  
Bing Xue
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Scales ◽  
Space Time ◽  
Air Monitoring ◽  
Quality Analysis ◽  
Population Exposure ◽  
Exposure Risk ◽  
Pollution Risk ◽  
Regional Air Pollution

In order to formulate policies to control regional air pollution and promote sustainable human–land system development, it is crucial to study the space–time distribution of air pollution and the population exposure risk. Existing studies are limited to individual fine particulate pollutants, which does not fully reflect the comprehensiveness of air quality. In addition, the spatiotemporal distribution of air quality and population exposure risk at different scales need to be further quantified. In this study, we used air monitoring station data and population spatial distribution data to analyze the spatiotemporal characteristics of air quality, including seasonal variations, variations before and during heating periods, and the occurrence frequency of priority pollutants in the traditional industrial areas of Northeast China in 2015. The population exposure–air pollution risk (PE-APR) model was used to calculate the population exposure risk at different spatial scales. The results suggest that GIS methods and air monitoring data help to establish a comprehensive air quality analysis framework, revealing spring–summer differentiation and the change trend of air quality with latitude. There are significant clustering features of air quality. A grid-scale population exposure–air pollution risk map is not restricted by administrative boundaries, which helps to discover high-risk areas of the main regional economic corridors and differences between inner cities and suburbs. This study provides a reference for understanding the space–time evolution of regional air pollution and formulating coordinated cross-regional air pollution strategies.

CAMS-Net: The Clean Air Monitoring and Solutions Network

2021 ◽  
Author(s):  
Daniel Westervelt ◽  
Celeste McFarlane ◽  
Faye McNeill ◽  
R (Subu) Subramanian ◽  
Mike Giordano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Low Cost ◽  
Air Monitoring ◽  
Quality Data ◽  
Calibration Methods ◽  
The World ◽  
Clean Air

<p>There is a severe lack of air pollution data around the world. This includes large portions of low- and middle-income countries (LMICs), as well as rural areas of wealthier nations as monitors tend to be located in large metropolises. Low cost sensors (LCS) for measuring air pollution and identifying sources offer a possible path forward to remedy the lack of data, though significant knowledge gaps and caveats remain regarding the accurate application and interpretation of such devices.</p><p>The Clean Air Monitoring and Solutions Network (CAMS-Net) establishes an international network of networks that unites scientists, decision-makers, city administrators, citizen groups, the private sector, and other local stakeholders in co-developing new methods and best practices for real-time air quality data collection, data sharing, and solutions for air quality improvements. CAMS-Net brings together at least 32 multidisciplinary member networks from North America, Europe, Africa, and India. The project establishes a mechanism for international collaboration, builds technical capacity, shares knowledge, and trains the next generation of air quality practitioners and advocates, including domestic and international graduate students and postdoctoral researchers. </p><p>Here we present some preliminary research accelerated through the CAMS-Net project. Specifically, we present LCS calibration methodology for several co-locations in LMICs (Accra, Ghana; Kampala, Uganda; Nairobi, Kenya; Addis Ababa, Ethiopia; and Kolkata, India), in which reference BAM-1020 PM2.5 monitors were placed side-by-side with LCS. We demonstrate that both simple multiple linear regression calibration methods for bias-correcting LCS and more complex machine learning methods can reduce bias in LCS to close to zero, while increasing correlation. For example, in Kampala, Raw PurpleAir PM2.5 data are strongly correlated with the BAM-1020 PM2.5 (r<sup>2</sup> = 0.88), but have a mean bias of approximately 12 μg m<sup>-3</sup>. Two calibration models, multiple linear regression and a random forest approach, decrease mean bias from 12 μg m<sup>-3 </sup>to -1.84 µg m<sup>-3</sup> or less and improve the the r<sup>2</sup> from 0.88 to 0.96. We find similar performance in several other regions of the world. Location-specific calibration of low-cost sensors is necessary in order to obtain useful data, since sensor performance is closely tied to environmental conditions such as relative humidity. This work is a first step towards developing a database of region-specific correction factors for low cost sensors, which are exploding in popularity globally and have the potential to close the air pollution data gap especially in resource-limited countries. </p><p> </p><p> </p>

Two models and two emission databases – evaluation of the PM10 and PM2.5 concentrations modelled with WRF-Chem and EMEP4PL

2020 ◽  
Author(s):  
Małgorzata Werner ◽  
Maciej Kryza ◽  
Justyna Dudek
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Model Performance ◽  
Correlation Coefficients ◽  
Chemical Transport ◽  
Population Exposure ◽  
Transport Models ◽  
Local Database ◽  
Chemical Transport Models ◽  
Pm10 And Pm2.5

<p>Some European countries in Eastern or Central Europe, such as Poland, have serious problems with air quality. High concentrations of particulate matter (PM) in winter are often related to high coal and wood combustion for residential heating. Meteorological conditions, i.e. low air temperature and anticyclones, provide favourable conditions for the accumulation of air pollution, rendering it harmful to people.  PM concentrations during the warmer period are much lower, however there are episodes with elevated concentrations related to e.g. long-range transport of pollutants from biomass burning areas. Policy makers in Poland put a lot of effort to improve air quality as well as inform and aware people on harmful effects of air pollution. One of the relevant tools which provides information on the past, current and future state of the air pollution are chemical transport models.</p><p>In this study we aim for validation of PM10 and PM2.5 concentrations from two different chemical transport models – WRF-Chem and EMEP4PL and two different emission databases – a) a regional EMEP database, and b) a local database provided by the Chief Inspectorate of Environmental Pollution. Modelled PM10 and PM2.5 concentrations were compared with observations from Polish stations for the year 2018. The results show a clear seasonal variation of the models performance with the lowest correlation coefficients in summer. Higher seasonal variability is observed for WRF-Chem than EMEP, which is probably related to differences in calculations of boundary layer height. Application of local database improves the results for both models. For several months, the performance of WRF-Chem and EMEP is clearly different, which shows that an ensemble approach with an application of these two models could improve the modelling results. The differences in the model performance significantly influence the results of the population exposure assessment.</p><p> </p>

scholarly journals Source influence on emission pathways and ambient PM<sub>2.5</sub> pollution over India (2015–2050)

2018 ◽  
Vol 18 (11) ◽  
pp. 8017-8039 ◽  
Author(s):  
Chandra Venkataraman ◽  
Michael Brauer ◽  
Kushal Tibrewal ◽  
Pankaj Sadavarte ◽  
Qiao Ma ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Energy Demand ◽  
Biomass Combustion ◽  
Anthropogenic Source ◽  
Population Exposure ◽  
Future Economic Development ◽  
Future Air Pollution

Abstract. India is currently experiencing degraded air quality, and future economic development will lead to challenges for air quality management. Scenarios of sectoral emissions of fine particulate matter and its precursors were developed and evaluated for 2015–2050, under specific pathways of diffusion of cleaner and more energy-efficient technologies. The impacts of individual source sectors on PM2.5 concentrations were assessed through systematic simulations of spatially and temporally resolved particulate matter concentrations, using the GEOS-Chem model, followed by population-weighted aggregation to national and state levels. We find that PM2.5 pollution is a pan-India problem, with a regional character, and is not limited to urban areas or megacities. Under present-day emissions, levels in most states exceeded the national PM2.5 annual standard (40 µg m−3). Sources related to human activities were responsible for the largest proportion of the present-day population exposure to PM2.5 in India. About 60 % of India's mean population-weighted PM2.5 concentrations come from anthropogenic source sectors, while the remainder are from other sources, windblown dust and extra-regional sources. Leading contributors are residential biomass combustion, power plant and industrial coal combustion and anthropogenic dust (including coal fly ash, fugitive road dust and waste burning). Transportation, brick production and distributed diesel were other contributors to PM2.5. Future evolution of emissions under regulations set at current levels and promulgated levels caused further deterioration of air quality in 2030 and 2050. Under an ambitious prospective policy scenario, promoting very large shifts away from traditional biomass technologies and coal-based electricity generation, significant reductions in PM2.5 levels are achievable in 2030 and 2050. Effective mitigation of future air pollution in India requires adoption of aggressive prospective regulation, currently not formulated, for a three-pronged switch away from (i) biomass-fuelled traditional technologies, (ii) industrial coal-burning and (iii) open burning of agricultural residue. Future air pollution is dominated by industrial process emissions, reflecting larger expansion in industrial, rather than residential energy demand. However, even under the most active reductions envisioned, the 2050 mean exposure, excluding any impact from windblown mineral dust, is estimated to be nearly 3 times higher than the WHO Air Quality Guideline.

scholarly journals Mapping Anisotropic Landscape for Understanding Underlying Spatial Structure of Air Pollution

2019 ◽  
Vol 1 ◽  
pp. 1-1
Author(s):  
Chen-Hsuan Tu ◽  
Tzai-Hung Wen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Statistical Approach ◽  
Space Time ◽  
Grid Resolution ◽  
Sensor Data ◽  
Small Scale ◽  
Physical Factors ◽  
Physical Interactions ◽  
Time Grid

<p><strong>Abstract.</strong> Urban air pollution problem has become a huge threat to human health in the most developing and developed countries. Therefore, monitoring air quality with high spatial and temporal resolutions is an important issue. There are two different approaches to mapping street-level distributions of air quality in time and space. One is mathematical approach, which uses numerical methods to calculate the concentration of air pollutants in each space-time grid through considering chemical transport, wind field, terrain morphology and other parameters which affect the direction and intensity of dispersion. This approach is limited by intensively computational process, so most of studies used either rough spatial grid resolution for representing large-scale regions or detailed grid resolution for small-scale areas. Numerical models with rough grid resolution could not capture detailed physical interactions in the micro-environment. The other approach is statistical approach, which used spatial interpolation techniques, such as inverse distance weighting (IDW) and Kriging methods, or established regression models, such as land-use regression (LUR), for deriving concentrations of air pollution from remote sensing or ground-level station sensor data. This approach is assumed linear associations with environmental factors and isotropic distance-decayed phenomena, which also ignores complex physical interactions.</p><p>Spatial distribution of air pollution could be affected by directional background factors, such as wind fields, surface relief and so on. The spatial effects of these physical factors are not isotropic. However, recent studies used statistical modelling approaches are based on isotropic assumptions and did not consider directional variations of these factors on air quality. The purpose of the study is to develop an innovative statistical approach to measure directional effects on air quality with spatial heterogeneity. We produces anisotropic landscapes of directional fields for identifying major directions for each space-time grid through EPA’s monitoring station data to visualize space-time trend of air quality changing with directions. This study provides significant insight for understanding spatial structures behind air pollution distributions influenced by directional physical factors.</p>

scholarly journals Urban Population Exposure to Air Pollution in Europe Over the Last Decades

2020 ◽  
Author(s):  
Pierre Sicard ◽  
Evgenios Agathokleous ◽  
Alessandra De Marco ◽  
Elena Paoletti ◽  
Vicent Calatayud
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Population ◽  
Quality Standards ◽  
Ambient Air ◽  
World Health ◽  
Population Exposure ◽  
Ambient Air Quality ◽  
Air Quality Standards ◽  
The Eu

Abstract Background - The paper presents an overview of air quality in the 27 member countries of the European Union (EU) and the United Kingdom (previous EU-28), from 2000 to 2017. We reviewed the progress made towards meeting the air quality standards established by the EU Ambient Air Quality Directives (Directive 2008/50/EC) and the World Health Organization (WHO) Air Quality Guidelines by estimating the trends (Mann-Kendal test) in national emissions of main air pollutants, urban population exposure to air pollution, and in mortality related to exposure to ambient fine particles (PM2.5) and tropospheric ozone (O3). Results - Despite significant reductions of emissions (e.g. sulfur oxides: ~80%, nitrogen oxides: ~46%, non-methane volatile organic compounds: ~44%, particulate matters with a diameter lower than 2.5µm and 10µm: ~30%), the EU-28 urban population was exposed to PM2.5 and O3 levels widely exceeding the WHO limit values for the protection of human health. Between 2000 and 2017, the annual PM2.5-related number of deaths decreased (- 4.85 per 106 inhabitants) in line with a reduction of PM2.5 levels observed at urban air quality monitoring stations. The rising O3 levels became a major public health issue in the EU-28 cities where the annual O3-related number of premature deaths increased (+ 0.55 deaths per 106 inhabitants). Conclusions - To achieve the objectives of the Ambient Air Quality Directives and mitigate air pollution impacts, actions need to be urgently taken at all governance levels. In this context, greening and re‐naturing cities can help meet air quality standards, but also answer to social needs, as recently highlighted by the COVID-19 lockdowns.

scholarly journals Using geo-targeted social media data to detect outdoor air pollution

2016 ◽  
Vol XLI-B2 ◽  
pp. 553-554 ◽  
Author(s):  
W. Jiang ◽  
Y. Wang ◽  
M. H. Tsou ◽  
X. Fu
Keyword(s):  
Social Media ◽  
Air Pollution ◽  
Air Quality ◽  
Environmental Changes ◽  
Real Space ◽  
Air Monitoring ◽  
Outdoor Air ◽  
Outdoor Air Pollution ◽  
Media Messages ◽  
Monitoring Stations

Outdoor air pollution has become a more and more serious issue over recent years (He, 2014). Urban air quality is measured at air monitoring stations. Building air monitoring stations requires land, incurs costs and entails skilled technicians to maintain a station. Many countries do not have any monitoring stations and even lack any means to monitor air quality. Recent years, the social media could be used to monitor air quality dynamically (Wang, 2015; Mei, 2014). However, no studies have investigated the inter-correlations between real-space and cyberspace by examining variation in micro-blogging behaviors relative to changes in daily air quality. Thus, existing methods of monitoring AQI using micro-blogging data shows a high degree of error between real AQI and air quality as inferred from social media messages. &lt;br&gt;&lt;br&gt; In this paper, we introduce a new geo-targeted social media analytic method to (1) investigate the dynamic relationship between air pollution-related posts on Sina Weibo and daily AQI values; (2) apply Gradient Tree Boosting, a machine learning method, to monitor the dynamics of AQI using filtered social media messages. Our results expose the spatiotemporal relationships between social media messages and real-world environmental changes as well suggesting new ways to monitor air pollution using social media.

scholarly journals Impact of Vehicle Fleet Modernization on the Traffic-Originated Air Pollution in an Urban Area—A Case Study

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1581
Author(s):  
Piotr Holnicki ◽  
Zbigniew Nahorski ◽  
Andrzej Kałuszko
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Road Traffic ◽  
Substantial Contribution ◽  
Population Exposure ◽  
Passenger Cars ◽  
Emission Mitigation ◽  
Emission Standards ◽  
Vehicle Fleet ◽  
Co Concentrations

The main subject of this paper is an analysis of the influence of changes in the air pollution caused by road traffic, due to its modernization, on the air quality in Warsaw conurbation, Poland. Using the Calpuff model, simulations of the yearly averaged concentrations of NOx, CO, PM10, and PM2.5 were performed, together with an assessment of the population exposure to individual pollutions. Source apportionment analysis indicates that traffic is the main source of NOx and CO concentrations in the city atmosphere. Utilizing the Euro norms emission standards, a scenario of vehicle emission abatement is formulated based on the assumed general vehicle fleet modernization and transition to Euro 6 emission standards. Computer simulations show a reduction in NOx concentrations attributed to emission mitigation of passenger cars, trucks and vans, and public transport buses, respectively. On the other hand, improving air quality in terms of CO concentrations depends almost exclusively on gasoline vehicle modernization. The implementation of the considered scenario causes an adequate reduction in the population exposure and related health effects. In particular, implementation of the scenario discussed results in a 47% reduction (compared with the baseline value) in the attributable yearly deaths related to NOx pollution. In spite of a substantial contribution of vehicle traffic to the overall PM pollution, modernization of the fuel combustion causes only minor final effects because the dominant share of PM pollution in Warsaw originates from the municipal sector and the transboundary inflow.

scholarly journals Integrated systems for forecasting urban meteorology, air pollution and population exposure

2007 ◽  
Vol 7 (3) ◽  
pp. 855-874 ◽  
Author(s):  
A. Baklanov ◽  
O. Hänninen ◽  
L. H. Slørdal ◽  
J. Kukkonen ◽  
N. Bjergene ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Population Exposure ◽  
Quality Information ◽  
Urban Air ◽  
Initial Development

Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

scholarly journals Evaluating the Impact of a Wall-Type Green Infrastructure on PM10 and NOx Concentrations in an Urban Street Environment

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 839
Author(s):  
Maria Gabriella Villani ◽  
Felicita Russo ◽  
Mario Adani ◽  
Antonio Piersanti ◽  
Lina Vitali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Green Infrastructure ◽  
High Performance ◽  
Urban Setting ◽  
Population Exposure ◽  
Urban Street ◽  
Microscale Model ◽  
The Impact ◽  
Active Filtration

Nature-based solutions can represent beneficial tools in the field of urban transformation for their contribution to important environmental services such as air quality improvement. To evaluate the impact on urban air pollution of a CityTree (CT), an innovative wall-type green infrastructure in passive (deposition) and active (filtration) modes of operation, a study was conducted in a real urban setting in Modena (Italy) during 2017 and 2018, combining experimental measurements with modelling system evaluations. In this work, relying on the computational resources of CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID High Performance Computing infrastructure, we used the air pollution microscale model PMSS (Parallel Micro-SWIFT-Micro SPRAY) to simulate air quality during the experimental campaigns. The spatial characteristics of the impact of the CT on local air pollutants concentrations, specifically nitrogen oxides (NOx) and particulate matter (PM10), were assessed. In particular, we used prescribed bulk deposition velocities provided by the experimental campaigns, which tested the CT both in passive (deposition) and in active (filtration) mode of operation. Our results showed that the PM10 and NOx concentration reductions reach from more than 0.1% up to about 0.8% within an area of 10 × 20 m2 around the infrastructure, when the green infrastructure operates in passive mode. In filtration mode the CT exhibited higher performances in the abatement of PM10 concentrations (between 1.5% and 15%), within approximately the same area. We conclude that CTs may find an application in air quality hotspots within specific urban settings (i.e., urban street canyons) where a very localized reduction of pollutants concentration during rush hours might be of interest to limit population exposure. The optimization of the spatial arrangement of CT modules to increment the “clean air zone” is a factor to be investigated in the ongoing development of the CT technology.

scholarly journals Analysis of River Water and Air Pollution—Pljevlja as a “Hot Spot” of Montenegro

2021 ◽  
Vol 13 (9) ◽  
pp. 5229
Author(s):  
Miroslav Doderović ◽  
Dragan Burić ◽  
Ivan Mijanović ◽  
Marijan Premović
Keyword(s):  
Water Quality ◽  
Air Pollution ◽  
Air Quality ◽  
River Water ◽  
Hot Spot ◽  
Quality Analysis ◽  
Treatment Technology ◽  
Limit Values ◽  
Gas Treatment ◽  
Sources Of Pollution

The aim of the study was to gather information necessary for the examination of the river Ćehotina water quality as well as the air pollution in the urban area of Pljevlja (far north of Montenegro), from 2011 until 2018. The water quality of the Ćehotina River was observed by the Water Quality Index (WQI) method, based on ten physicochemical and microbiological parameters from five hydrological stations. In order to examine the air quality, we used data on the concentration of the PM10 particles from the station located in the center of Pljevlja. The obtained results of river water quality indicate that the situation was disturbing (bad quality dominates). The results of the air quality analysis indicate that the situation has been alarming and Pljevlja itself as a “hot spot” of Montenegro. Annual, seasonal and daily mean concentrations of PM10 particles were above the prescribed limit values, except during summer. Sources of pollution were mostly known, and in order to protect public health, it is necessary to take appropriate measures as soon as possible, primarily the introduction of modern exhaust gas treatment technology TPP ‘‘Pljevlja’’ and construction of a heating plant that would replace numerous individual (home) fireplaces in Pljevlja.

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