scholarly journals The Impact of “Coal to Gas” Policy on Air Quality: Evidence from Beijing, China

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3876 ◽  
Author(s):  
Zhe Liu ◽  
Xueli Chen ◽  
Jinyang Cai ◽  
Tomas Baležentis ◽  
Yue Li
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Energy Use ◽  
Northern China ◽  
Heating System ◽  
Use Efficiency ◽  
The Government ◽  
The Impact ◽  
Beijing China

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The “coal to gas” policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the “coal to gas” policy on air quality. Strong evidence shows that the “coal to gas” policy has significantly improved the air quality in Beijing. On average, the “coal to gas” policy reduced sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the “coal to gas” policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the “coal to gas” policy, so as to alleviate the air pollution in Beijing, China.

scholarly journals Evidence on the Impact of Winter Heating Policy on Air Pollution and Its Dynamic Changes in North China

2019 ◽  
Vol 11 (10) ◽  
pp. 2728 ◽  
Author(s):  
Shulin Wang ◽  
Yongtao Li ◽  
Mahfuzul Haque
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Central Government ◽  
Northern China ◽  
Policy Changes ◽  
Dynamic Changes ◽  
The Public ◽  
Local Residents ◽  
The Impact

Environmental pollution, especially air pollution, is an alarming issue for the public, which is extensively debated among academic scholars. During the winter heating season, “smog” has become somewhat a normal phenomenon to local residents’ livelihood in northern China. Based on the daily air pollution data of regional cities in China from 2014 to 2016, and using a regression discontinuity design (RDD), the study finds that winter heating makes the air quality worse in the northern part of China. With the start of the winter heating, it increases the Air Quality Index (AQI) by 10.4%, particulate matter smaller than 10 μm (PM10) by 9.77%, particulate matter smaller than 2.5 μm (PM2.5) by 17.25%, CO by 9.84%, NO2 by 5.23%, and SO2 by 17.1%. Furthermore, dynamic changes demonstrate that air quality has gradually improved due to a series of heating policy changes implemented by the central government in recent years. Specifically, from 2014 to 2016, major indicators measuring the air pollution decrease dramatically, such as AQI by 92.36%, PM10 by 91.24%, PM2.5 by 84.06%, CO by 70.97%, NO2 by 52.76%, and SO2 by 17.15%.

scholarly journals Effect of the uncertainty in meteorology on air quality model predictions

Időjárás ◽  
2021 ◽  
Vol 125 (4) ◽  
pp. 625-646
Author(s):  
Zita Ferenczi ◽  
Emese Homolya ◽  
Krisztina Lázár ◽  
Anita Tóth
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Model System ◽  
Air Quality Model ◽  
Quality Model ◽  
Near Surface ◽  
The Impact

An operational air quality forecasting model system has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the area of Hungary and three big cites of the country (Budapest, Miskolc, and Pécs). The core of the model system is the CHIMERE off-line chemical transport model. The AROME numerical weather prediction model provides the gridded meteorological inputs for the chemical model calculations. The horizontal resolution of the AROME meteorological fields is consistent with the CHIMERE horizontal resolution. The individual forecasted concentrations for the following 2 days are displayed on a public website of the Hungarian Meteorological Service. It is essential to have a quantitative understanding of the uncertainty in model output arising from uncertainties in the input meteorological fields. The main aim of this research is to probe the response of an air quality model to its uncertain meteorological inputs. Ensembles are one method to explore how uncertainty in meteorology affects air pollution concentrations. During the past decades, meteorological ensemble modeling has received extensive research and operational interest because of its ability to better characterize forecast uncertainty. One such ensemble forecast system is the one of the AROME model, which has an 11-member ensemble where each member is perturbed by initial and lateral boundary conditions. In this work we focus on wintertime particulate matter concentrations, since this pollutant is extremely sensitive to near-surface mixing processes. Selecting a number of extreme air pollution situations we will show what the impact of the meteorological uncertainty is on the simulated concentration fields using AROME ensemble members.

The Impact of Building Tianfu New Area on Chengdu City Air Quality

2013 ◽  
Vol 726-731 ◽  
pp. 1396-1399 ◽  
Author(s):  
Ping Liu ◽  
Hong Ling Guo
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Positive Measure ◽  
Air Pollutant ◽  
The Government ◽  
And Control ◽  
The Impact

This study analyzed the impact of building Tianfu new area in Sichuan on Chengdu city air quality. The paper insists the characteristics of terrain and climate in Tianfu new area lead to the difficult to convect and diffuse air pollutant, and the trend of air quality deteriorating has already emerged as the activities of a large number of industries and population. Absolutely ,building Tianfu new area will further aggravate this trend. And the paper suggests the government should take positive measure in management and policy to prevent and control air pollution.

scholarly journals Association between long term exposure to particulate matter and incident hypertension in Spain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Viyey Doulatram-Gamgaram ◽  
Sergio Valdés ◽  
Cristina Maldonado-Araque ◽  
Ana Lago-Sampedro ◽  
Rocío Badía-Guillén ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Transport Model ◽  
Population Based ◽  
Incident Hypertension ◽  
Particulate Pollution ◽  
Adjusted Model ◽  
The Impact

AbstractExposure to air particulate matter has been linked with hypertension and blood pressure levels. The metabolic risks of air pollution could vary according to the specific characteristics of each area, and has not been sufficiently evaluated in Spain. We analyzed 1103 individuals, participants in a Spanish nationwide population based cohort study ([email protected]), who were free of hypertension at baseline (2008–2010) and completed a follow-up exam of the cohort (2016–2017). Cohort participants were assigned air pollution concentrations for particulate matter < 10 μm (PM10) and < 2.5 μm (PM2.5) during follow-up (2008–2016) obtained through modeling combined with measurements taken at air quality stations (CHIMERE chemistry-transport model). Mean and SD concentrations of PM10 and PM2.5 were 20.17 ± 3.91 μg/m3 and 10.83 ± 2.08 μg/m3 respectively. During follow-up 282 cases of incident hypertension were recorded. In the fully adjusted model, compared with the lowest quartile of PM10, the multivariate weighted ORs (95% CIs) for developing hypertension with increasing PM10 exposures were 0.82 (0.59–1.14), 1.28 (0.93–1.78) and 1.45 (1.05–2.01) in quartile 2, 3 and 4 respectively (p for a trend of 0.003). The corresponding weighted ORs according to PM2.5 exposures were 0.80 (0.57–1.13), 1.11 (0.80–1.53) and 1.48 (1.09–2.00) (p for trend 0.004). For each 5-μg/m3 increment in PM10 and PM2.5 concentrations, the odds for incident hypertension increased 1.22 (1.06–1.41) p = 0.007 and 1.39 (1.07–1.81) p = 0.02 respectively. In conclusion, our study contributes to assessing the impact of particulate pollution on the incidence of hypertension in Spain, reinforcing the need for improving air quality as much as possible in order to decrease the risk of cardiometabolic disease in the population.

scholarly journals Five-Year Analysis of Air Pollution in Istanbul Including Also the Impact of the COVID-19 Pandemic

2021 ◽  
Author(s):  
Nilüfer Aykaç ◽  
Pınar Pazarlı Bostan ◽  
Sabri Serhan Olcay ◽  
Berker Öztürk
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Nitrogen Oxide ◽  
Air Pollutants ◽  
World Health ◽  
Limit Values ◽  
Public Data ◽  
Health Organization ◽  
The Impact

INTRODUCTION: Particulate matter, sulfur dioxide, ozone, and nitrogen oxide compounds are the main air pollutants. The purpose of this research is to analyze the five-year air quality of Istanbul and examine the effect of movement restrictions due to the COVID-19 pandemic on pollutants. METHODS: The public data of the National Air Quality Observation Network has been utilized. The research has been conducted based on the five-year daily averages of PM10, NO2, and NOx pollutants for Istanbul between 2016 - 2020. The data of stations which measured for 75% and more throughout the year has been used. The effect of lockdowns enforced due to COVID-19 was revealed by comparing data of pollutants from April and May of 2020 to the same period in 2019. RESULTS: There were 12 stations between 2016 – 2018, and 39 stations in 2019 and 2020 which measured particulate matter and nitrogen oxide compounds. Only 9 stations reached the standard of measuring pollution for 75% and more throughout the year. The PM10, NO2, and NOx levels measured by all the 9 stations between 2016 - 2020 are above the limit values set by the World Health Organization (WHO). The lockdowns in 2020 have not been helping improvements in air pollution issue. However, there have been regressions of 33.4%, 59.6%, and 52.6% in the overall average particulate matter, nitrogen oxide, and nitrogen dioxide concentrations during the lockdowns between 23-26 of April, 1-3 of May, and 23-26 of May, respectively. DISCUSSION AND CONCLUSION: The air pollution issue in Istanbul has not improved in a meaningful and significant manner for the last five years. There is a significant deficiency in measuring traffic pollution. It has been found that two days long lockdowns and physical movement restrictions due to COVID-19 have significantly contributed to a significant regression in the overall concentration of air pollutants.

scholarly journals Greenery Planning for Improvement of Urban Air Quality—A Review

Proceedings ◽  
2019 ◽  
Vol 16 (1) ◽  
pp. 13 ◽  
Author(s):  
Kamil Leszek Rawski
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Urban Air Quality ◽  
Urban Air ◽  
Foreign Literature ◽  
Urban Greenery ◽  
The Subject ◽  
The Impact ◽  
Effective Planning

On the basis of Polish and foreign literature on the subject, the impact of vegetation on air pollution (e.g., particulate matter) was described, as well as what significance its proper arrangement has. Reviewing findings carried out by various researchers, the criteria of selecting plants were collected and specified. Only those criteria that contribute to obtaining optimal results in the fight against air pollution were taken into consideration. Also, based on the collected data, a set of guidelines was developed that could eventually serve as a tool for more effective planning of urban greenery.

scholarly journals The impact of measures to reduce ambient air PM<sub>10</sub> concentrations originating from road dust, evaluated for a street canyon in Helsinki

2019 ◽  
Vol 19 (17) ◽  
pp. 11199-11212 ◽  
Author(s):  
Ana Stojiljkovic ◽  
Mari Kauhaniemi ◽  
Jaakko Kukkonen ◽  
Kaarle Kupiainen ◽  
Ari Karppinen ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Street Canyon ◽  
Dust Emission ◽  
Road Dust ◽  
Ambient Air ◽  
Air Quality Measurements ◽  
Emission Models ◽  
The Impact ◽  
Quality Measurements

Abstract. We have numerically evaluated how effective selected potential measures would be for reducing the impact of road dust on ambient air particulate matter (PM10). The selected measures included a reduction of the use of studded tyres on light-duty vehicles and a reduction of the use of salt or sand for traction control. We have evaluated these measures for a street canyon located in central Helsinki for four years (2007–2009 and 2014). Air quality measurements were conducted in the street canyon for two years, 2009 and 2014. Two road dust emission models, NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and FORE (Forecasting Of Road dust Emissions), were applied in combination with the Operational Street Pollution Model (OSPM), a street canyon dispersion model, to compute the street increments of PM10 (i.e. the fraction of PM10 concentration originating from traffic emissions at the street level) within the street canyon. The predicted concentrations were compared with the air quality measurements. Both road dust emission models reproduced the seasonal variability of the PM10 concentrations fairly well but under-predicted the annual mean values. It was found that the largest reductions of concentrations could potentially be achieved by reducing the fraction of vehicles that use studded tyres. For instance, a 30 % decrease in the number of vehicles using studded tyres would result in an average decrease in the non-exhaust street increment of PM10 from 10 % to 22 %, depending on the model used and the year considered. Modelled contributions of traction sand and salt to the annual mean non-exhaust street increment of PM10 ranged from 4 % to 20 % for the traction sand and from 0.1 % to 4 % for the traction salt. The results presented here can be used to support the development of optimal strategies for reducing high springtime particulate matter concentrations originating from road dust.

scholarly journals Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

2021 ◽  
Author(s):  
Christian Acal ◽  
Ana M. Aguilera ◽  
Annalina Sarra ◽  
Adelia Evangelista ◽  
Tonio Di Battista ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Functional Data ◽  
Central Italy ◽  
Principal Component ◽  
Functional Principal Component Analysis ◽  
Area Of Interest ◽  
Pm 10 ◽  
The Impact ◽  
Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

scholarly journals The interplay between environmental exposures and COVID-19 risks in the health of children

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Peter D. Sly ◽  
Brittany A. Trottier ◽  
Catherine M. Bulka ◽  
Stephania A. Cormier ◽  
Julius Fobil ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Disease Severity ◽  
Research Effort ◽  
Severe Disease ◽  
Environmental Exposures ◽  
Research Strategy ◽  
Lessons Learned ◽  
Research Questions ◽  
The Impact

Abstract Background An unusual feature of SARS-Cov-2 infection and the COVID-19 pandemic is that children are less severely affected than adults. This is especially paradoxical given the epidemiological links between poor air quality and increased COVID-19 severity in adults and that children are generally more vulnerable than adults to the adverse consequences of air pollution. Objectives To identify gaps in knowledge about the factors that protect children from severe SARS-Cov-2 infection even in the face of air pollution, and to develop a transdisciplinary research strategy to address these gaps. Methods An international group of researchers interested in children’s environmental health was invited to identify knowledge gaps and to develop research questions to close these gaps. Discussion Key research questions identified include: what are the effects of SAR-Cov-2 infection during pregnancy on the developing fetus and child; what is the impact of age at infection and genetic susceptibility on disease severity; why do some children with COVID-19 infection develop toxic shock and Kawasaki-like symptoms; what are the impacts of toxic environmental exposures including poor air quality, chemical and metal exposures on innate immunity, especially in the respiratory epithelium; what is the possible role of a “dirty” environment in conveying protection – an example of the “hygiene hypothesis”; and what are the long term health effects of SARS-Cov-2 infection in early life. Conclusion A concerted research effort by a multidisciplinary team of scientists is needed to understand the links between environmental exposures, especially air pollution and COVID-19. We call for specific research funding to encourage basic and clinical research to understand if/why exposure to environmental factors is associated with more severe disease, why children appear to be protected, and how innate immune responses may be involved. Lessons learned about SARS-Cov-2 infection in our children will help us to understand and reduce disease severity in adults, the opposite of the usual scenario.

scholarly journals Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

2021 ◽  
Author(s):  
Shwet Ketu ◽  
Pramod Kumar Mishra
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Classification Algorithm ◽  
Quality Data ◽  
Pollution Level ◽  
Classification Problems ◽  
Chi Square ◽  
The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

Sign in / Sign up

Export Citation Format

Share Document