Prenatal exposure to ambient air pollution and adverse birth outcomes: An umbrella review of 36 systematic reviews and meta-analyses.

The goal of this paper is to prove the necessity for a more thorough consideration and more active use of the modern ICTs for the technological support for the practical implementation of the SDGs’ ecological block in the aspect of the increase of energy efficiency and environmental pollution control. The originality of this paper is as follows: it develops a proprietary methodology of evaluating the technical readiness (level of the development of ICTs) for implementing the ecological block of the SDGs, which envisage the increase of energy efficiency and the growth of environmental pollution control. The highest (but moderate) technological readiness to implement the ecological block of the SDGs among developed countries has been shown by Canada (14.42 points) and Denmark (11.03 points), among developing countries – China (7.72 points). As a result, it has been proved that UCTs are a perspective tool of the practical implementation of the ecological block of the SDGs, stimulating the increase of energy efficiency and improving the environment pollution control. For developed countries, it is recommended to increase the use of Big data and AI analytics by 90.36% and increase the use of ICTs in E-government by 89.74%. This paper’s contribution to literature consists in filling the gap of the uncertainty (underdevelopment) of the tools for practical implementation of the SDGs ecological block. This paper has demonstrated the substantial potential of ICTs in the stimulation of the growth of energy efficiency and reduction of environmental pollution in developed and developing countries.

Energy Efficiency and Pollution Control Through ICTs for Sustainable Development

Evaporation rates and pollutants emission from heated cooking oils and influencing factors.

In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.

Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR

Current research on activities of daily living (ADLs) disability has mostly focused on the analysis of demographic characteristics, while research on the microcharacteristics of individuals and the macroenvironment is relatively limited, and these studies solely concern the impact of air quality on individual health.This study innovatively investigated the impact of air quality on ADL disability by matching micro data of individuals from the China Health and Retirement Longitudinal Study with data of urban environmental quality from 122 cities. In this study, an ordered panel logit model was adopted for the benchmark test, and the two-stage ordered probit model with IV was used for endogenous treatment.This innovative study investigated the impact of air quality on ADL disability by matching individual micro data from the China Health and Retirement Longitudinal Study with urban environmental quality data for 122 cities. The results showed that air quality significantly increased the probability of ADL disability. The positive and marginal effect of air quality on moderate and mild disability was higher. Generally, the marginal effect of air quality on residents' health was negative. In terms of group heterogeneity, the ADL disability of individuals aged over 60 years, those in the high Gross Domestic Product (GDP) group, females, and those in the nonpilot long-term care insurance group was more affected by air quality, and the interaction between air quality and serious illness showed that the deterioration of air quality exacerbated the ADL disability caused by serious illness; that is, the moderating effect was significant.According to the equilibrium condition of the individual health production function, the ADL disability caused by a 1% improvement in air quality is equivalent to the ADL disability caused by an 89.9652% reduction in serious illness, indicating that the effect of improved air quality is difficult to replace by any other method. Therefore, good air quality can not only reduce ADL disability directly but also reduce serious illness indirectly, which is equivalent to the reduction of ADL disability. This is called the health impact. 

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Determining the effect of air quality on activities of daily living disability: using tracking survey data from 122 cities in China

Air pollution control efficacy and health impacts: A global observational study from 2000 to 2016.

Background: PM2.5 concentrations vary between countries with similar CO2 emissions, possibly due to differences in air pollution control efficacy. However, no indicator of the level of air pollution control efficacy has yet been developed. We aimed to develop such an indicator, and to evaluate its global and temporal distribution and its association with country-level health metrics. Method: A novel indicator, ground level population-weighted average PM2.5 concentration per unit CO2 emission per capita, (written as PC in abbreviation ), was developed to assess country-specific air pollution control efficacy. We estimated and mapped the global average distribution of PC and PC changes during 2000-2016 across 196 countries. Pearson correlation coefficients and Generalized Additive Mixed Model (GAMM) were used to evaluate the relationship between PC and health metrics. Results: PC varied by country with an inverse association with the economic development. PC showed an almost stable trend globally from 2000 to 2016 with the low income groups increased. The Pearson correlation coefficients between PC and life expectancy at birth (LE), Infant-mortality rate (IMR), Under-five mortality rate (U5MR) and logarithm of GDP per capita (LPGDP) were -0.566, 0.646, 0.659, -0.585 respectively (all P-values <0.001). Compared with PM2.5 or CO2 , PC could explain more variation of LE, IMR and U5MR. The association between PC and health metrics was independent of GDP per capita. Conclusions: PC might be a good indicator for air pollution control efficacy and was related to important health indicators. Our findings provide a new way to interpret health inequity across the globe from the point of air pollution control efficacy. Keywords:air pollution, climate change, health inequity, air pollution control efficacy

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Zhongwen Zhang

Papers

Air pollution control efficacy and health impacts: A global observational study from 2000 to 2016.

Air pollution control efficacy and health impacts: A global observed study from 2000 to 2016