scholarly journals How can we improve air pollution? Try increasing trust first

2021 ◽  
pp. 1-21
Author(s):  
Bridget Lynn Hoffmann ◽  
Carlos Scartascini ◽  
Fernando G. Cafferata
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Survey Data ◽  
Mexico City ◽  
Environmental Policies ◽  
Short Term ◽  
The Government ◽  
Polluting Firms

Abstract Environmental policies are characterized by salient short-term costs and long-term benefits that are difficult to observe and to attribute to the government's efforts. These characteristics imply that citizens’ support for environmental policies is highly dependent on their trust in the government's capability to implement solutions and commitment to investments in those policies. Using novel survey data from Mexico City, we show that trust in the government is positively correlated with citizens’ willingness to support an additional tax approximately equal to a day's minimum wage to improve air quality and greater preference for government retention of revenues from fees collected from polluting firms. We find similar correlations using the perceived quality of public goods as a measure of government competence. These results provide evidence that mistrust can be an obstacle to better environmental outcomes.

scholarly journals How Can We Improve Air Pollution?: Try Increasing Trust First

2021 ◽  
Author(s):  
Fernando G. Cafferata ◽  
Bridget Lynn Hoffmann ◽  
Carlos Scartascini
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Survey Data ◽  
Mexico City ◽  
Environmental Policies ◽  
Short Term ◽  
The Government ◽  
Polluting Firms

Environmental policies are characterized by salient short-term costs and long-term benefits that are difficult to observe and to attribute to the government's efforts. These characteristics imply that citizens' support for environmental policies is highly dependent on their trust in the government's capability to implement solutions and commitment to investments in those policies. Using novel survey data from Mexico City, we show that trust in the government is positively correlated with citizens' willingness to support an additional tax approximately equal to a days minimum wage to improve air quality and greater preference for government retention of revenues from fees collected from polluting firms. We find similar correlations using the perceived quality of public goods as a measure of government competence. These results provide evidence that mistrust can be an obstacle to better environmental outcomes.

scholarly journals Effects of meteorology and emissions on urban air quality: a quantitative statistical approach to long-term records (1999–2016) in Seoul, South Korea

2018 ◽  
Vol 18 (21) ◽  
pp. 16121-16137 ◽  
Author(s):  
Jihoon Seo ◽  
Doo-Sun R. Park ◽  
Jin Young Kim ◽  
Daeok Youn ◽  
Yong Bin Lim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
South Korea ◽  
Meteorological Conditions ◽  
Meteorological Variables ◽  
Urban Air ◽  
Short Term ◽  
Long Term Trends ◽  
Local Emissions

Abstract. Together with emissions of air pollutants and precursors, meteorological conditions play important roles in local air quality through accumulation or ventilation, regional transport, and atmospheric chemistry. In this study, we extensively investigated multi-timescale meteorological effects on the urban air pollution using the long-term measurements data of PM10, SO2, NO2, CO, and O3 and meteorological variables over the period of 1999–2016 in Seoul, South Korea. The long-term air quality data were decomposed into trend-free short-term components and long-term trends by the Kolmogorov–Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using a multiple linear regression with meteorological variables. In terms of short-term variability, intercorrelations among the pollutants and meteorological variables and composite analysis of synoptic meteorological fields exhibited that the warm and stagnant conditions in the migratory high-pressure system are related to the high PM10 and primary pollutant, while the strong irradiance and low NO2 by high winds at the rear of a cyclone are related to the high O3. In terms of long-term trends, decrease in PM10 (−1.75 µg m−3 yr−1) and increase in O3 (+0.88 ppb yr−1) in Seoul were largely contributed by the meteorology-related trends (−0.94 µg m−3 yr−1 for PM10 and +0.47 ppb yr−1 for O3), which were attributable to the subregional-scale wind speed increase. Comparisons with estimated local emissions and socioeconomic indices like gross domestic product (GDP) growth and fuel consumptions indicate probable influences of the 2008 global economic recession as well as the enforced regulations from the mid-2000s on the emission-related trends of PM10 and other primary pollutants. Change rates of local emissions and the transport term of long-term components calculated by the tracer continuity equation revealed a decrease in contributions of local emissions to the primary pollutants including PM10 and an increase in contributions of local secondary productions to O3. The present results not only reveal an important role of synoptic meteorological conditions on the episodic air pollution events but also give insights into the practical effects of environmental policies and regulations on the long-term air pollution trends. As a complementary approach to the chemical transport modeling, this study will provide a scientific background for developing and improving effective air quality management strategy in Seoul and its metropolitan area.

scholarly journals Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities

2021 ◽  
Vol 66 ◽  
Author(s):  
Ru Cao ◽  
Yuxin Wang ◽  
Xiaochuan Pan ◽  
Xiaobin Jin ◽  
Jing Huang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pollution Level ◽  
Short Term ◽  
Migration Index ◽  
Long Run ◽  
Long Term Impact

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries.Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations.Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed.Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

scholarly journals Survey of knowledge and attitude of people about air pollution in Tehran, Iran

2019 ◽  
Author(s):  
Ali Ahmadfazeli ◽  
Zohreh Hesami ◽  
Ali Ghanbari ◽  
Mohammad Safari ◽  
Mohammad Sadegh Has-sanvand
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Negative Impact ◽  
Negative Influence ◽  
Further Education ◽  
Air Pollution Control ◽  
Significant Negative Impact ◽  
Knowledge And Attitude ◽  
The Government

Introduction: The importance of air quality and paying attention to what we breathe have been valuable always. So that air pollution is one of the key environmental issues in urban communities. Several studies show that the potential effects of air pollution on human health include increased mortality and changes in cardiovascular and respiratory functions.   Materials and methods: The population of this study was people in 22 dis-tricts of Tehran megacity. The questionnaires were placed at the municipality centers of 22 districts and randomly completed by people who came to the center. Questions included the importance of air pollution, the comparison of air quality with last year, the main sources of air pollution, the problems created by air pollution, the quality of informing system, as well as questions about their satisfaction about government’s actions on air pollution control plans. Data analysis was performed using SPSS 24.   Results: 84.14 % of the participants stated that air pollution is important to them and has a negative influence on their lives. Also, most of them were not satisfied with the measures taken and expected that actions would be better to reduce air pollution. 91.10 % of the participants considered cars as the main causes of air pollution. Also, 68.22 % of people believed that air pollution had a significant negative impact on their quality of life.   Conclusion: Most people are willing to live at a higher cost but a less pol-luted environment, while more of them are not well aware of their role in reducing air pollution. Moreover further education should be provided on the role of people in reducing air pollution. Additionally, the government must deal with air pollutants and take serious measures.

COVID-19 Lockdown and the Aerosphere in India: Lessons Learned on How to Reduce Air Pollution

2021 ◽  
Author(s):  
Subhasmita Panda ◽  
Priyadatta Satpathy ◽  
Trutpi Das ◽  
Boopathy Ramasamy
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Atmospheric Chemistry ◽  
Surface Ozone ◽  
Lessons Learned ◽  
Oxides Of Nitrogen ◽  
Comprehensive Overview ◽  
Constant Operation ◽  
The Government

The giant increase in COVID-19 infection across India forced the government to impose strict lockdown in order to curb the pandemic. Although the stringent restrictions crippled India’s economy and poor people’s livelihood, it significantly improved the air quality of most of the polluted cities of India and rejuvenated the atmosphere. Thus, the major objective of this study is to provide a comprehensive overview of lockdown on pollutants prevailing in the atmosphere. A prominent decline in primary pollutants such as Particulate matter (PM), Black carbon (BC), Oxides of nitrogen (NOx), Carbon monoxide (CO) is observed across the country. However, lockdown had a trifling impact on Sulphur dioxide (SO2) concentration over some parts of India due to the constant operation of coal-fired thermal plants as a part of essential service. Furthermore, the sudden decline in NOx concentration disturbed the complex atmospheric chemistry and lead to an enhancement of surface ozone (O3) (secondary pollutant) in many cities of India. Thus, lockdown emerged as a unique opportunity for the atmospheric researchers, policymakers as well as stakeholders to collect baseline data of pollutants and their major sources. This will help to set new targets of air quality standards and to develop various mitigation processes to combat air pollution.

Monitoring and Prediction of Air Quality using Neural Network

2018 ◽  
Vol 8 (6) ◽  
pp. 47
Author(s):  
Shwetal Raipure
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Pollution Index ◽  
Learning Technologies ◽  
Short Term ◽  
Initial Information ◽  
Term Forecast ◽  
The Neural Network

Air Quality monitoring is very important in today s world. There are many harmful pollutants present in the air which are very harmful for human health. Prolonged consumption of such air may lead to severe death and harmful diseases. It is also harmful for crops as well as animals which may damage natural environment. There are  several pollutants which are present in the air that decreases the quality of air such as sulfur oxide, nitrogen dioxide, carbon monoxide and dioxide, and particulate matter. Neural Network  can be used for prediction of population for short term as well as long term using a deep learning technologies. Neural network specify two types of predictive models. the first one is the a temporal which is for short-term forecast of the pollutants in the air for the short coming or nearest days and the second one is  a spatial forecast of atmospheric pollution index in any point of city. The artificial neural networks takes initial information and consider the hidden dependencies are used to improve the efficiency and accuracy of the ecology management decisions. In this paper the forecasting of atmospheric air pollution index in industrial cities based on the  neural network model has proposed.

scholarly journals Air Pollution and Mortality: Timing Is Everything

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1274
Author(s):  
Frederick W. Lipfert
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Indoor Air Pollution ◽  
Health Effect ◽  
Traffic Density ◽  
Long Term Effects ◽  
Short Term ◽  
Long Term Studies

This paper considers timing issues in health-effect exposure and response studies. Short-term studies must consider delayed and cumulative responses; prior exposures, disease latency, and cumulative impacts are required for long-term studies. Lacking individual data, long-term air quality describes locations, as do greenspaces and traffic density, rather than exposures of residents. Indoor air pollution can bias long-term exposures and effect estimates but short-term effects also respond to infiltrated outdoor air. Daily air quality fluctuations may affect the frail elderly and are necessarily included in long-term averages; any true long-term effects must be given by differences between annual and daily effects. I found such differences to be negligible after adjusting for insufficient lag effects in time-series studies and neglect of prior exposures in long-term studies. Aging of subjects under study implies cumulative exposures, but based on age-specific mortality, I found relative risks decreasing with age, precluding cumulative effects. A new type of time-series study found daily mortality of previously frail subjects to be associated with various pollutants without exposure thresholds, but the role of air pollution in the onset of frailty remains an unexplored issue. The importance of short-term fluctuations has been underestimated and putative effects of long-term exposures have been overestimated.

Research Insights: What Role Does Trust in Government Play in Support for Public Policies to Improve Air Pollution?

2021 ◽  
Author(s):  
Fernando G. Caferatta ◽  
Bridget Hoffman ◽  
Carlos Scartascini
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Public Services ◽  
Public Policies ◽  
Public Spending ◽  
Perceived Quality ◽  
Trust In Government ◽  
Private Goods ◽  
Polluting Firms

Trust in government and the perceived quality of public services are positively correlated with support for an additional tax to improve air quality. Trust in government and the perceived quality of public services are positively correlated with a preference for government retention of revenue from fees collected from polluting firms as opposed to distribution of revenue directly to citizens. Trust in government and the perceived quality of public services are not significantly correlated with citizens preferences on the allocation of those revenues between public spending and private goods.

scholarly journals Dealing with Voters’ Privacy Preferences and Readiness in Electronic Voting

2018 ◽  
Vol 11 (3) ◽  
pp. 994
Author(s):  
Muharman Lubis ◽  
Arif Ridho Lubis
Keyword(s):  
Initial Step ◽  
Electronic Voting ◽  
Short Term ◽  
Privacy Preferences ◽  
The Social ◽  
Election System ◽  
Voting System ◽  
The Government

<p class="MsoNormal" style="text-align: justify; text-indent: 36.0pt;">Various countries have been encouraged to adopt electronic voting because it can reduce operational cost and time spent for tabulation process. In the current research, it has been mentioned several problem arised in term of technical aspects, voters’ trust, machine vulnerabilities and privacy right in which experts argued the election system have been compromised. In short term, the certain faction will try to exploit the system weaknesses for their own benefit, while in the long term, it can create public distrust to the government, which decrease the voters turn out, break the participation willingness and downgrade the quality of voting. Thus, the government should deal with previous issues in the election before adopting electronic approach while at the same time align with voters’ expectation to provide better election in serve citizen through comprehensive analysis. This study provide initial step to analyse the readiness of electronic voting from the social perspective in response to how Indonesia view the initiative to adopt new tech in voting system.</p>

scholarly journals Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

2022 ◽  
Author(s):  
Zhen Zhang ◽  
Shiqing Zhang ◽  
Xiaoming Zhao ◽  
Linjian Chen ◽  
Jun Yao
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Air Quality ◽  
Attention Mechanism ◽  
Quality Prediction ◽  
Temporal Difference ◽  
Short Term ◽  
Air Quality Prediction ◽  
Complex Relationships

Abstract The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

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