Forecasting Urban Meteorology, Air Pollution and Population Exposure (European FUMAPEX Project)

2007 ◽  
pp. 29-40
Author(s):  
A. Baklanov ◽  
N. Bjergene ◽  
B. Fay ◽  
S. Finardi ◽  
A. Gross ◽  
...  
Keyword(s):  
Air Pollution ◽  
Population Exposure ◽  
Urban Meteorology

Vicious Cycle of Economic Growth, Pm2.5 and COVID-19 Case of G7 Countries

2020 ◽  
Author(s):  
Rıdvan Karacan
Keyword(s):  
Economic Growth ◽  
Air Pollution ◽  
Particulate Matter ◽  
Fossil Fuels ◽  
Fine Particulate Matter ◽  
Panel Cointegration ◽  
Air Pollutant ◽  
Population Exposure ◽  
Causality Analysis ◽  
G7 Countries

<p>Today, production is carried out depending on fossil fuels. Fossil fuels pollute the air as they contain high levels of carbon. Many studies have been carried out on the economic costs of air pollution. However, in the present study, unlike the former ones, economic growth's relationship with the COVID-19 virus in addition to air pollution was examined. The COVID-19 virus, which was initially reported in Wuhan, China in December 2019 and affected the whole world, has caused many cases and deaths. Researchers have been going on studying how the virus is transmitted. Some of these studies suggest that the number of virus-related cases increases in regions with a high level of air pollution. Based on this fact, it is thought that air pollution will increase the number of COVID-19 cases in G7 Countries where industrial production is widespread. Therefore, the negative aspects of economic growth, which currently depends on fossil fuels, is tried to be revealed. The research was carried out for the period between 2000-2019. Panel cointegration test and panel causality analysis were used for the empirical analysis. Particulate matter known as PM2.5[1] was used as an indicator of air pollution. Consequently, a positive long-term relationship has been identified between PM2.5 and economic growth. This relationship also affects the number of COVID-19 cases.</p><p><br></p><p><br></p><p>[1] "Fine particulate matter (PM2.5) is an air pollutant that poses the greatest risk to health globally, affecting more people than any other pollutant (WHO, 2018). Chronic exposure to PM2.5 considerably increases the risk of respiratory and cardiovascular diseases in particular (WHO, 2018). For these reasons, population exposure to (outdoor or ambient) PM2.5 has been identified as an OECD Green Growth headline indicator" (OECD.Stat).</p>

Assessment of population exposure to air pollution by benzene

2007 ◽  
Vol 210 (3-4) ◽  
pp. 407-410 ◽  
Author(s):  
Oxana Tchepel ◽  
Ana Penedo ◽  
Madalena Gomes
Keyword(s):  
Air Pollution ◽  
Population Exposure

scholarly journals Population Exposure to Ambient PM2.5 at the Subdistrict Level in China

2018 ◽  
Vol 15 (12) ◽  
pp. 2683 ◽  
Author(s):  
Ying Long ◽  
Jianghao Wang ◽  
Kang Wu ◽  
Junjie Zhang
Keyword(s):  
Air Pollution ◽  
Action Plan ◽  
Population Data ◽  
Spatiotemporal Variability ◽  
World Health ◽  
Health Concern ◽  
Population Exposure ◽  
Air Pollution Control ◽  
Annual Average ◽  
Potential Population

Fine-particulate pollution is a major public health concern in China. Accurate assessment of the population exposed to PM2.5 requires high-resolution pollution and population information. This paper assesses China’s potential population exposure to PM2.5, maps its spatiotemporal variability, and simulates the effects of the recent air pollution control policy. We relate satellite-based Aerosol Optical Depth (AOD) retrievals to ground-based PM2.5 observations. We employ block cokriging (BCK) to improve the spatial interpolation of PM2.5 distribution. We use the subdistrict level population data to estimate and map the potential population exposure to PM2.5 pollution in China at the subdistrict level, the smallest administrative unit with public demographic information. During 8 April 2013 and 7 April 2014, China’s population-weighted annual average PM2.5 concentration was nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1322 million people, or 98.6% of the total population, were exposed to PM2.5 at levels above WHO’s daily guideline for longer than half a year. If China can achieve its Action Plan on Prevention and Control of Air Pollution targets by 2017, the population exposed to PM2.5 above China’s daily standard for longer than half a year will be reduced by 85%.

scholarly journals Distribution of sources of household air pollution: a cross-sectional study in Cameroon

2021 ◽  
Author(s):  
Esong Miranda Baame ◽  
André Pascal Goura ◽  
Bertrand Hugo Ngahane Mbatchou ◽  
Berenice Walage ◽  
Herman Styve Yomi Simo ◽  
...  
Keyword(s):  
Air Pollution ◽  
Rural Communities ◽  
Health District ◽  
Household Air Pollution ◽  
Easy Access ◽  
Population Exposure ◽  
Liquefied Petroleum Gas ◽  
Cross Sectional Survey ◽  
Related Factors ◽  
Cross Sectional

Abstract Background: Household air pollution (HAP) is a recognised risk factor for many diseases, including respiratory diseases, cardiovascular/circulatory disorders, adverse pregnancy outcomes and cataracts. Population exposure to biomass fuels, including wood, varies among countries and from one fuel source to the other. This study aimed to investigate the different sources of HAP in peri-urban and rural communities in Cameroon. Methods: A cross-sectional survey was conducted in a representative sample of households from the Dschang Health District (DHD) region. This included 848 homes in which a range of fuels for cooking including biomass (firewood, charcoal, sawdust), kerosene and liquefied petroleum gas (LPG) were used both indoors and outdoors. Results: Of the study households, 651 (77%) reported exclusive use of firewood and 141 (17%) reported using more than one source of fuel. Exclusive use of firewood was greater in rural communities (94%) than in peri-urban communities (38%). In peri-urban communites, use of multiple fuels including LPG, wood, sawdust and kerosene, was more common (44.75%). A total of 25.03% of households in both peri-urban and rural communities reported using bottled gas (or liquified petroleum gas (LPG) for cooking. Motivations for choice of fuel included, price, availability (easy access), rapidity, tradition or culture related factorsConclusion: Wood is the main cooking fuel in both peri-urban and rural communities in the Dschang Health District. Work to help households (especially those who are resource poor) to adopt LPG equipment for cooking, and use in a more exclusive way is required.

scholarly journals A two-stage residential location and transport mode choice model with exposure to traffic-induced air pollution

2021 ◽  
Author(s):  
Mirjam Schindler ◽  
JYT Wang ◽  
RD Connors
Keyword(s):  
Air Pollution ◽  
Choice Model ◽  
Mode Choice ◽  
Residential Location ◽  
Health Concern ◽  
Spatially Explicit ◽  
Population Exposure ◽  
Population Densities ◽  
Transport Mode ◽  
Mode Choice Model

Air pollution is an increasing concern to urban residents. In response, residents are beginning to adapt their travel behaviour and to consider local air quality when choosing a home. We study implications of such behaviour for the morphology of cities and population exposure to traffic-induced air pollution. To do so, we propose a spatially explicit and integrated residential location and transport mode choice model for a city with traffic-induced air pollution. Intra-urban spatial patterns of population densities, transport mode choices, and resulting population exposure are analysed for urban settings of varying levels of health concern and air pollution information available to residents. Numerical analysis of the feedback between residential location choice and transport mode choice, and between residents' choices and the subsequent potential impact on their own health suggests that increased availability of information on spatially variable traffic-induced health concerns shifts population towards suburban areas with availability of public transport. Thus, health benefits result from reduced population densities close to urban centres in this context. To mitigate population exposure, our work highlights the need for spatially explicit information on peoples' air pollution concerns and, on this basis, spatially differentiated integrated land use and transport measures.

Changing Population Exposure to Pollution in China’s Special Economic Zones

2021 ◽  
Vol 111 ◽  
pp. 406-409
Author(s):  
Leslie A. Martin ◽  
Katie Zhang
Keyword(s):  
Air Pollution ◽  
Population Growth ◽  
Environmental Quality ◽  
Recent Literature ◽  
Industrial Clusters ◽  
Location Decisions ◽  
Population Exposure ◽  
Special Economic Zones ◽  
Economic Zones

Martin and Zhang (2020) show that the increase in manufacturing output in China's special economic zones significantly increased air pollution. In this complementary paper, we document that widespread migration into these industrial clusters between 2000 and 2010 did little to change overall population exposure to pollution. We show using satellite air pollution data that there is much heterogeneity across zones and that most population growth occurred in relatively cleaner zones, supporting recent literature that documents local willingness to make location decisions based on environmental quality (Chen et al. 2019, Khanna et al. 2020).

scholarly journals Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

2020 ◽  
Vol 17 (6) ◽  
pp. 2099 ◽  
Author(s):  
Martin Otto Paul Ramacher ◽  
Matthias Karl
Keyword(s):  
Air Pollution ◽  
Population Dynamics ◽  
Urban Areas ◽  
Road Traffic ◽  
Ambient Air ◽  
Traffic Emissions ◽  
Exposure Model ◽  
Population Exposure ◽  
Road Traffic Emissions ◽  
Pollutant Concentrations

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen’s health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO2 and 19% more to PM2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO2 and PM2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO2 and 15% for PM2.5). Results for population-weighted exposure revealed exposure to PM2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from −12% to +7% for NO2 and PM2.5. The developed “dynamic transport approach” is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

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

&lt;p&gt;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. &amp;#160;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.&lt;/p&gt;&lt;p&gt;In this study we aim for validation of PM10 and PM2.5 concentrations from two different chemical transport models &amp;#8211; WRF-Chem and EMEP4PL and two different emission databases &amp;#8211; 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.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document