Characteristics of Air Pollutant Emissions and Distribution for Particulate Matter Concentration of Air Pollution Networks in Gyeongsangbuk-do

2021 ◽  
Vol 37 (3) ◽  
pp. 536-551
Author(s):  
InJo Hwang ◽  
Tae-Jung Lee ◽  
TaeOh Kim ◽  
Gwi-Nam Bae
2015 ◽  
Vol 15 (19) ◽  
pp. 11411-11432 ◽  
Author(s):  
G. Janssens-Maenhout ◽  
M. Crippa ◽  
D. Guizzardi ◽  
F. Dentener ◽  
M. Muntean ◽  
...  

Abstract. The mandate of the Task Force Hemispheric Transport of Air Pollution (TF HTAP) under the Convention on Long-Range Transboundary Air Pollution (CLRTAP) is to improve the scientific understanding of the intercontinental air pollution transport, to quantify impacts on human health, vegetation and climate, to identify emission mitigation options across the regions of the Northern Hemisphere, and to guide future policies on these aspects. The harmonization and improvement of regional emission inventories is imperative to obtain consolidated estimates on the formation of global-scale air pollution. An emissions data set has been constructed using regional emission grid maps (annual and monthly) for SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC and OC for the years 2008 and 2010, with the purpose of providing consistent information to global and regional scale modelling efforts. This compilation of different regional gridded inventories – including that of the Environmental Protection Agency (EPA) for USA, the EPA and Environment Canada (for Canada), the European Monitoring and Evaluation Programme (EMEP) and Netherlands Organisation for Applied Scientific Research (TNO) for Europe, and the Model Inter-comparison Study for Asia (MICS-Asia III) for China, India and other Asian countries – was gap-filled with the emission grid maps of the Emissions Database for Global Atmospheric Research (EDGARv4.3) for the rest of the world (mainly South America, Africa, Russia and Oceania). Emissions from seven main categories of human activities (power, industry, residential, agriculture, ground transport, aviation and shipping) were estimated and spatially distributed on a common grid of 0.1° × 0.1° longitude-latitude, to yield monthly, global, sector-specific grid maps for each substance and year. The HTAP_v2.2 air pollutant grid maps are considered to combine latest available regional information within a complete global data set. The disaggregation by sectors, high spatial and temporal resolution and detailed information on the data sources and references used will provide the user the required transparency. Because HTAP_v2.2 contains primarily official and/or widely used regional emission grid maps, it can be recommended as a global baseline emission inventory, which is regionally accepted as a reference and from which different scenarios assessing emission reduction policies at a global scale could start. An analysis of country-specific implied emission factors shows a large difference between industrialised countries and developing countries for acidifying gaseous air pollutant emissions (SO2 and NOx) from the energy and industry sectors. This is not observed for the particulate matter emissions (PM10, PM2.5), which show large differences between countries in the residential sector instead. The per capita emissions of all world countries, classified from low to high income, reveal an increase in level and in variation for gaseous acidifying pollutants, but not for aerosols. For aerosols, an opposite trend is apparent with higher per capita emissions of particulate matter for low income countries.


2020 ◽  
Vol 117 (32) ◽  
pp. 18984-18990 ◽  
Author(s):  
Zander S. Venter ◽  
Kristin Aunan ◽  
Sourangsu Chowdhury ◽  
Jos Lelieveld

The lockdown response to coronavirus disease 2019 (COVID-19) has caused an unprecedented reduction in global economic and transport activity. We test the hypothesis that this has reduced tropospheric and ground-level air pollution concentrations, using satellite data and a network of >10,000 air quality stations. After accounting for the effects of meteorological variability, we find declines in the population-weighted concentration of ground-level nitrogen dioxide (NO2: 60% with 95% CI 48 to 72%), and fine particulate matter (PM2.5: 31%; 95% CI: 17 to 45%), with marginal increases in ozone (O3: 4%; 95% CI: −2 to 10%) in 34 countries during lockdown dates up until 15 May. Except for ozone, satellite measurements of the troposphere indicate much smaller reductions, highlighting the spatial variability of pollutant anomalies attributable to complex NOxchemistry and long-distance transport of fine particulate matter with a diameter less than 2.5 µm (PM2.5). By leveraging Google and Apple mobility data, we find empirical evidence for a link between global vehicle transportation declines and the reduction of ambient NO2exposure. While the state of global lockdown is not sustainable, these findings allude to the potential for mitigating public health risk by reducing “business as usual” air pollutant emissions from economic activities. Explore trends here:https://nina.earthengine.app/view/lockdown-pollution.


Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 739
Author(s):  
Bin Xu ◽  
Xiangyu You ◽  
Yaoyu Zhou ◽  
Chunhao Dai ◽  
Zhan Liu ◽  
...  

As one of China’s emerging urban agglomerations, the Changzhutan urban area is suffering from regional composite air pollution. Previous studies mainly focus on single cities or world-class urban agglomerations, which cannot provide a scientific basis for air pollution in emerging urban agglomerations. This paper proposes the latest high-resolution emission inventory through the emission factor method and compares the results with the rest of the urban agglomeration. The emission inventory shows that the estimates for sulfur dioxide (SO2), nitrogen oxides (NOX), particulate matter 10 (PM10), particulate matter 2.5 (PM2.5), volatile organic compounds (VOCs), and ammonia (NH3) emission are 132.5, 148.9, 111.6, 56.5, 119.0, and 72.0 kt, respectively. From the 3 × 3 km emission grid, the spatial difference of air pollutant emissions in the Changzhutan urban agglomeration was more obvious, but the overall trend of monthly pollutant discharge was relatively stable. Depending on the source apportionment, SO42−, OC, and NO3− are the main chemical constituents of PM2.5, accounting for 13.06, 8.24, and 4.84 μg/m3, respectively. Simultaneously, industrial emissions, vehicle exhaust, and dust are still three main sources that cannot be ignored. With the support of these data, the results of this study may provide a reference for other emerging urban agglomerations in air quality.


2020 ◽  
Vol 1 (2) ◽  
pp. 107-113
Author(s):  
N.V. Krishna Prasad ◽  
M.S.S.R.K.N. Sarma ◽  
P. Sasikala ◽  
Naga Raju M ◽  
N. Madhavi

Particulate matter concentration and its study has gained tremendous significance in view of increase in air pollution. Since air pollution has many adverse effects on mankind, measures may be taken by observing the trends in PM2.5 (particulate matter) and concentrations of pollutants like NO2, SO2, NO2, NO, NOx, CO, NH3 and RH(Relative Humidity)  as well as temperature. Even though continuous monitoring of air pollution in urban locations has been increasing in view of its huge impact on the sustainable development and ecological balance a regression model is essential always to analyse large sets of data. These regression models also play vital role in some cases where data was not observed due to unavoidable circumstances and during times when the measuring instruments do not work. In this context an attempt was made to develop a regression model exclusively for Visakhapatnam(India) a coastal, urban and industrial station and to analyse the trends in particulate matter concentration at this staion. A regression model was developed with PM2.5 as dependent variable and SO2, NOx, NO2, CO, NH3, temperature(Temp) and relative humidity(RH) as independent variables. The efficiency of the model was tested with known independent variables and PM2.5 was estimated. It is found that observed and estimated PM2.5 values are highly correlated.


2020 ◽  
Author(s):  
Rıdvan Karacan

<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>


Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 580
Author(s):  
Eyal Fattal ◽  
Hadas David-Saroussi ◽  
Ziv Klausner ◽  
Omri Buchman

The accumulated particulate matter concentration at a given vertical column due to traffic sources in urban area has many important consequences. This task, however, imposes a major challenge, since the problem of realistic pollutant dispersion in an urban environment is a very demanding task, both theoretically and computationally. This is mainly due to the highly inhomogeneous three dimensional turbulent flow regime in the urban canopy roughness sublayer, which is far from “local equilibrium” between shear production and dissipation. We present here a mass-consistent urban Lagrangian stochastic model for pollutants dispersion, where the flow field is modeled using a hybrid approach by which we model the surface layer based on the typical turbulent scales, both of the canopy and in the surface layer inertial sub-layer. In particular it relies on representing the canopy aerodynamically as a porous medium by spatial averaging the equations of motion, with the assumption that the canopy is laterally uniform on a scale much larger than the buildings but smaller than the urban block/neighbourhood, i.e., at the sub-urban-block scale. Choosing the spatial representative averaging volume allows the averaged variables to reflect the characteristic vertical heterogeneity of the canopy but to smooth out smaller scale spatial fluctuations caused as air flows in between the buildings. This modeling approach serves as the base for a realistic and efficient methodology for the calculation of the accumulated concentration from multiple traffic sources for any vertical column in the urban area. The existence of multiple traffic sources impose further difficulty since the computational effort required is very demanding for practical uses. Therefore, footprint analysis screening was introduced to identify the relevant part of the urban area which contributes to the chosen column. All the traffic sources in this footprint area where merged into several areal sources, further used for the evaluation of the concentration profile. This methodology was implemented for four cases in the Tel Aviv metropolitan area based on several selected summer climatological scenarios. We present different typical behaviors, demonstrating combination of source structure, urban morphology, flow characteristics, and the resultant dispersion pattern in each case.


2021 ◽  
Vol 67 (7) ◽  
pp. 2140-2150
Author(s):  
V. Sreekanth ◽  
Meenakshi Kushwaha ◽  
Padmavati Kulkarni ◽  
Adithi R. Upadhya ◽  
B. Spandana ◽  
...  

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