Long-Term Exposure to Industrial Air Pollution Emissions and the Incidence of Childhood Asthma: The Use of a Population-Based Birth Cohort and Dispersion Modeling

China is in a strategic phase of an industrial green transformation. Industrial air pollution is a key environmental target for governance. Because import trade is a core channel through which advanced environmental protection technology is absorbed, the question of whether technology spillovers brought about by import trade can reduce industrial air pollution emissions is a topic worth exploring. This paper uses a generalized spatial two-stage least-square (GS2SLS) model to explore the impact of import trade technology spillovers on industrial air pollution emission intensities using panel data from 30 provinces and cities between 2000 and 2017. Economic scale, industrial structure, and technological innovation are used as intermediary variables to test whether they play mediating effects. The results show that: (1) capital and intermediate goods technology spillovers directly reduce industrial air pollution emission intensity and (2) import trade technology spillovers indirectly reduce emission intensities by expanding economic scale, optimizing industrial structure, and enhancing technological innovation through mediating variables. Furthermore, industrial structure optimization and technological innovation have the largest mediating effects on industrial SO2, while economic expansion has the most significant mediating effect on industrial smoke and dust. The mediating effects of technology spillovers from intermediate goods exceed those of capital technology spillovers. Finally, industrial air pollution emission intensity demonstrates both spatial agglomeration and time lag effects. Environmental regulations and energy structure are shown to increase industrial air pollution emissions, while urbanization and foreign direct investment reduce industrial air pollution. Based upon these research results, some pertinent policy implications are proposed for China.

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Decomposition and measures of the driving factors for China’s industrial air pollution emissions at the prefectural level

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/467/1/012174 ◽

2020 ◽

Vol 467 ◽

pp. 012174

Author(s):

Zhimin Zhou

Keyword(s):

Air Pollution ◽

Driving Factors ◽

Industrial Air Pollution ◽

Pollution Emissions ◽

Air Pollution Emissions

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Socio-economic inequalities in exposure to industrial air pollution emissions in Quebec public schools

Can J Public Health ◽

10.17269/cjph.108.6166 ◽

2017 ◽

Vol 108 (5-6) ◽

pp. e503-e509 ◽

Cited By ~ 3

Author(s):

Emmanuelle Batisse ◽

Sophie Goudreau ◽

Jill Baumgartner ◽

Audrey Smargiassi

Keyword(s):

Air Pollution ◽

Public Schools ◽

Industrial Air Pollution ◽

Pollution Emissions ◽

Air Pollution Emissions ◽

Economic Inequalities

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Long-Term Exposure to PM2.5 and Cognitive Decline: A Longitudinal Population-Based Study

Journal of Alzheimer s Disease ◽

10.3233/jad-200852 ◽

2021 ◽

pp. 1-9

Author(s):

Giulia Grande ◽

Jing Wu ◽

Petter L.S. Ljungman ◽

Massimo Stafoggia ◽

Tom Bellander ◽

...

Keyword(s):

Air Pollution ◽

Cognitive Decline ◽

Population Based ◽

Cerebrovascular Diseases ◽

Air Pollutant ◽

National Study ◽

Piecewise Regression ◽

Risk Increase ◽

Restricted Cubic Splines

Background: A growing but contrasting evidence relates air pollution to cognitive decline. The role of cerebrovascular diseases in amplifying this risk is unclear. Objectives: 1) Investigate the association between long-term exposure to air pollution and cognitive decline; 2) Test whether cerebrovascular diseases amplify this association. Methods: We examined 2,253 participants of the Swedish National study on Aging and Care in Kungsholmen (SNAC-K). One major air pollutant (particulate matter ≤2.5μm, PM2.5) was assessed yearly from 1990, using dispersion models for outdoor levels at residential addresses. The speed of cognitive decline (Mini-Mental State Examination, MMSE) was estimated as the rate of MMSE decline (linear mixed models) and further dichotomized into the upper (25%fastest cognitive decline), versus the three lower quartiles. The cognitive scores were used to calculate the odds of fast cognitive decline per levels of PM2.5 using regression models and considering linear and restricted cubic splines of 10 years exposure before the baseline. The potential modifier effect of cerebrovascular diseases was tested by adding an interaction term in the model. Results: We observed an inverted U-shape relationship between PM2.5 and cognitive decline. The multi-adjusted piecewise regression model showed an increased OR of fast cognitive decline of 81%(95%CI = 1.2–3.2) per interquartile range difference up to mean PM2.5 level (8.6μg/m3) for individuals older than 80. Above such level we observed no further risk increase (OR = 0.89;95%CI = 0.74–1.06). The presence of cerebrovascular diseases further increased such risk by 6%. Conclusion: Low to mean PM2.5 levels were associated with higher risk of accelerated cognitive decline. Cerebrovascular diseases further amplified such risk.

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COVID-19 and Air Pollution: Measuring Pandemic Impact to Air Quality in Five European Countries

Atmosphere ◽

10.3390/atmos12030290 ◽

2021 ◽

Vol 12 (3) ◽

pp. 290

Author(s):

Akvilė Feiferytė Skirienė ◽

Žaneta Stasiškienė

Keyword(s):

Air Pollution ◽

Air Quality ◽

Social Services ◽

Pollution Monitoring ◽

Air Pollution Monitoring ◽

Rapid Spread ◽

Air Quality Improvement ◽

Environment Agency ◽

Pollution Emissions ◽

Air Pollution Emissions

The rapid spread of the coronavirus (COVID-19) pandemic affected the economy, trade, transport, health care, social services, and other sectors. To control the rapid dispersion of the virus, most countries imposed national lockdowns and social distancing policies. This led to reduced industrial, commercial, and human activities, followed by lower air pollution emissions, which caused air quality improvement. Air pollution monitoring data from the European Environment Agency (EEA) datasets were used to investigate how lockdown policies affected air quality changes in the period before and during the COVID-19 lockdown, comparing to the same periods in 2018 and 2019, along with an assessment of the Index of Production variation impact to air pollution changes during the pandemic in 2020. Analysis results show that industrial and mobility activities were lower in the period of the lockdown along with the reduced selected pollutant NO2, PM2.5, PM10 emissions by approximately 20–40% in 2020.

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