The expansion of China's development zones has made great contributions to economic development, as well as provided practical guidance for other developing countries to implement development zone policies. However, in the context of global advocacy of low carbon, literature about how the development zone policy affect carbon emissions is poor, especially in China at the urban level. Therefore, this study takes China's development zone policy as a quasi-natural experiment, using the panel data of 285 cities in China from 2003 to 2020, and adopting the DID model to analyze its impact on carbon emissions. After a series of robustness tests including placebo test, dynamic test (all independent variables are lagged by one period), endogeneity test, and parallel trend test, the results are basically robust. The findings show that the development zone policy indeed significantly reduces carbon emissions. In addition, we find that cities with higher resource endowments, cities in the eastern and central regions, and other larger cities across the country have better carbon emissions reduction effects. To a certain extent, the research in this paper fills the gap of theoretical research on carbon emissions in terms of the development zone policy, and provides some practical basis for future research in the field of carbon emissions.
Objective: Previous epidemiological studies have shown that both long-term and short-term exposure to fine particulate matters (PM2.5) were associated with the morbidity and mortality of circulatory system diseases (CSD). However, the impact of PM2.5 on CSD remains inconclusive. This study aimed to investigate the associations between PM2.5 and circulatory system diseases in Ganzhou.
Methods: We conducted this time series study to explore the association between ambient PM2.5 exposure and daily hospital admissions for CSD from 2016 to 2020 in Ganzhou by using generalized additive models (GAMs). Stratified analyses were also performed by gender, age, and season.
Results: Based on 201,799 hospitalized cases, significant and positive associations were found between short-term PM2.5 exposure and hospital admissions for CSD, including total CSD, hypertension, coronary heart disease (CHD), cerebrovascular disease (CEVD), heart failure (HF), and arrhythmia. Each 10 μg/m3 increase in PM2.5 concentrations was associated with a 2.588% (95% confidence interval [CI], 1.161%–4.035%), 2.773% (95% CI, 1.246%–4.324%), 2.865% (95% CI, 0.786%–4.893%), 1.691% (95% CI, 0.239%–3.165%), 4.173% (95% CI, 1.988%–6.404%) and 1.496% (95% CI, 0.030%–2.983%) increment in hospitalizations for total CSD, hypertension, CHD, CEVD, HF, and arrhythmia, respectively. As PM2.5 concentrations rise, the hospitalizations for arrhythmia showed a slow upward trend, while other CSD increased sharply at high PM2.5 levels. In subgroup analyses, the impacts of PM2.5 on hospitalizations for CSD were not materially changed, although the females had higher risks of hypertension, HF, and arrhythmia. The relationships between PM2.5 exposure and hospitalizations for CSD were more significant among individuals aged ≤65 years, except for arrhythmia. PM2.5 had stronger effects on total CSD, hypertension, CEVD, HF, and arrhythmia during cold seasons.
Conclusion: PM2.5 exposure was positively associated with daily hospital admissions for CSD, which might provide informative insight on adverse effects of PM2.5.
Introduction: There have been many researches done on the association between maternal exposure to ambient air pollution and adverse pregnancy outcomes, but few studies related to very low birth weight (VLBW). This study thus explores the association between maternal exposure to ambient air pollutants and the risk of VLBW, and estimates the sensitive exposure time window.
Methods: A retrospective cohort study analyzed in Chongqing, China, during 2015–2020. The Generalized Additive Model were applied to estimate exposures for each participant during each trimester and the entire pregnancy period.
Results: For each 10 μg/m3 increase in PM2.5 during pregnancy, the relative risk of VLBW increased on the first trimester, with RR = 1.100 (95% CI: 1.012, 1.195) in the single-pollutant model. Similarly, for each 10 μg/m3 increase in PM10, there was a 12.9% (RR = 1.129, 95% CI: 1.055, 1.209) increase for VLBW on the first trimester in the single-pollutant model, and an 11.5% (RR = 1.115, 95% CI: 1.024, 1.213) increase in the multi-pollutant model, respectively. The first and second trimester exposures of NO2 were found to have statistically significant RR values for VLBW. The RR values on the first trimester were 1.131 (95% CI: 1.037, 1.233) and 1.112 (95% CI: 1.015, 1.218) in the single-pollutant model and multi-pollutant model, respectively; The RR values on the second trimester were 1.129 (95% CI: 1.027, 1.241) and 1.146 (95% CI: 1.038, 1.265) in the single-pollutant model and multi-pollutant model, respectively. The RR of O3 exposure for VLBW on the entire trimester was 1.076 (95% CI: 1.010–1.146), and on the second trimester was 1.078 (95% CI: 1:016, 1.144) in the single-pollutant model.
Conclusion: This study indicates that maternal exposure to high levels of PM2.5, PM10, NO2, and O3 during pregnancy may increase the risk of very low birth weight, especially for exposure on the first and second trimester. Reducing the risk of early maternal exposure to ambient air pollution is thus necessary for pregnant women.
Pig farming has been a crucial contribution to China's food security although intestinal fermentation and its excrement during pig breeding are major sources of greenhouse gas emissions. In this paper, we measured the carbon emission efficiency of pig farming in 30 provinces (autonomous regions and municipalities) from 2010 to 2020 by using the non-expected output Slack-Based Measure (SBM) model and analyzed the spatial characteristics of the carbon emission efficiency of pig farming in China. We also examined and analyzed the factors influencing the carbon emission efficiency of pig farming by using the limited dependent variable model (Tobit). The results show that: the carbon emission efficiency of pig farming in China shows an M-shaped upward trend over time by comparing the carbon emission efficiency longitudinally during the study period and the carbon emission efficiency of pig farming shows a decreasing trend in the east, central and west regions of China by comparing the carbon emission efficiency of different regions horizontally. It's also shown that regions with low- and extremely-low-efficiency transfer from the east to the central and west regions and the central and regions with high-efficiency transfer to the east. The regression analysis of the factors influencing the carbon emission efficiency of pig breeding shows that the comparative advantage of the pig industry and transportation accessibility is positively correlated with the carbon emission efficiency of pig breeding, whereas the proportion of food resources and market scale is negatively correlated with the carbon emission efficiency of pig breeding. At the same time, the production layout index has no significant influence on the carbon emission efficiency of pig breeding. The research results provide a theoretical basis for regional differentiation of carbon emission management from pig farming, optimizing the layout of the pig industry and reducing environmental pollution.
Introduction: The concentrations of particulate and gaseous Polycyclic Hydrocarbons Carbon (PAHs) were determined in the urban atmosphere of Delhi in different seasons (winter, summer, and monsoon).
Methodology: The samples were collected using instrument air metric (particulate phase) and charcoal tube (gaseous phase) and analyzed through Gas chromatography. The principal component and correlation were used to identify the sources of particulate and gaseous PAHs during different seasons.
Results and discussion: The mean concentration of the sum of total PAHs (TPAHs) for particulate and gaseous phases at all the sites were found to be higher in the winter season (165.14 ± 50.44 ng/m3 and 65.73 ± 16.84 ng/m3) than in the summer season (134.08 ± 35.0 ng/m3 and 43.43 ± 9.59 ng/m3), whereas in the monsoon season the concentration was least (68.15 ± 18.25 ng/m3 and 37.63 1 13.62 ng/m3). The principal component analysis (PCA) results revealed that seasonal variations of PAHs accounted for over 86.9%, 84.5%, and 94.5% for the summer, monsoon, and winter seasons, respectively. The strong and positive correlation coefficients were observed between B(ghi)P and DahA (0.922), B(a)P and IcdP (0.857), and B(a)P and DahA (0.821), which indicated the common source emissions of PAHs. In addition to this, the correlation between Nap and Flu, Flu and Flt, B(a)P, and IcdP showed moderate to high correlation ranging from 0.68 to 0.75 for the particulate phase PAHs. The carcinogenic health risk values for gaseous and particulate phase PAHs at all sites were calculated to be 4.53 × 10−6, 2.36 × 10-5 for children, and 1.22 × 10−5, 6.35 × 10−5 for adults, respectively. The carcinogenic health risk for current results was found to be relatively higher than the prescribed standard of the Central Pollution Control Board, India (1.0 × 10−6).
Over the past five decades, the Green Revolution in India has been a great success resulting in significantly increased crop yields and food grain productivity. Northwestern India, also known as the country’s breadbasket, alone produces two-thirds of the wheat and rice grains under the crop rotation system. Our previous study has shown that the post-monsoon rice crop production in the Punjab state of India has increased by 25%. The crop yields produce proportionate amounts of residue, a large part of which is subjected to burn in the open fields due to the near-absence of a wide-scale, affordable, and environmentally sustainable removal mechanism. A significant increase in crop productivity coincides with a 60% increase in post-harvest crop residue burning during 2002–2016. The study also demonstrated a robust relationship between satellite measurements of vegetation index—a proxy for crop amounts, and post-harvest fires—a precursor of air pollution events, for predicting seasonal agricultural burning. In this report, the efficacy of the proposed prediction model is assessed by comparing the forecasted seasonal fire activity against the actual detection of active fires for the post-monsoon burning seasons of 2017–2021. A simple linear regression model allows efficient prediction of seasonal fire activity within an error of up to 10%. In addition to forecasting seasonal fire activity, the linear regression model offers a practical tool to track and evaluate the effectiveness of the residue management system intended to reduce fire activities and resulting air pollution.
Background: Fine particulate matter (PM2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM2.5 pollution is controversial in China.
Methods: In this study, we explored spatial-temporal characteristics and driving factors of PM2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR).
Results: PM2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m−3 a−1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM2.5 concentrations. The driving force of socioeconomic factors on PM2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013.
Conclusion: We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM2.5 emission and put forward effective policy to alleviate haze pollution.