Study on clean heating based on air pollution and energy consumption

Guo, Xiaopeng; Ren, Dongfang; Li, Cunbin

doi:10.1007/s11356-019-07093-8

Cited by 13 publications

(10 citation statements)

References 25 publications

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“…Regional development difference is one of the prominent issues in China’s sustainable and coordinated development [ 84 ]. Differences exist in the resource endowment and development path of different regions, leading to the industrial structures various in different regions [ 53 ]. Because the industrial structure has a decisive impact on resource consumption and pollutant emissions [ 85 , 86 ] and the content of the BSDW policy implemented in all cities is the same, we further classify cities according to industry types to estimate the emission reduction effect of the BSDW policy.…”

Section: Resultsmentioning

confidence: 99%

“…Huang et al [ 52 ] used hierarchical Bayesian models and daily data to estimate the health impacts of the “Action Plan for Air Pollution Prevention and Control” from 2013 to 2017 in 74 key cities in China, and found that annual average concentrations of PM 2.5 , SO 2 and CO decreased by 33.3%, 54.1%, 28.2%, while no significant changes were seen in NO x (9.7% reduction; 95% CI −23.0 to 42.4) or O 3 (20.4% increase; 95% CI −30.1 to 71.0). Guo et al [ 53 ] used the varying coefficient model to analyze the impact of clean electricity utilization on yearly air pollution in the BTH region and identify the long-term relationship between heating and air pollution. The results showed that the coefficient between coal consumption and pollutant emissions was positive, while electricity consumption was negatively correlated with pollutant emissions, which indicated that “replacing coal with electricity” in BTH region had played an active role in air pollution control.…”

Section: Literature Reviewmentioning

confidence: 99%

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Does the “Blue Sky Defense War Policy” Paint the Sky Blue?—A Case Study of Beijing–Tianjin–Hebei Region, China

Yang

Wang

Chen

et al. 2021

IJERPH

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Improving air quality is an urgent task for the Beijing–Tianjin–Hebei (BTH) region in China. In 2018, utilizing 365 days’ daily concentration data of six air pollutants (including PM2.5, PM10, SO2, NO2, CO and O3) at 947 air quality grid monitoring points of 13 cities in the BTH region and controlling the meteorological factors, this paper takes the implementation of the Blue Sky Defense War (BSDW) policy as a quasi-natural experiment to examine the emission reduction effect of the policy in the BTH region by applying the difference-in-difference method. Results show that the policy leads to the significant reduction of the daily average concentration of PM2.5, PM10, SO2, O3 by −1.951 μg/m3, −3.872 μg/m3, −1.902 μg/m3, −7.882 μg/m3 and CO by −0.014 mg/m3, respectively. The results of the robustness test support the aforementioned conclusions. However, this paper finds that the concentration of NO2 increases significantly (1.865 μg/m3). In winter heating seasons, the concentration of SO2, CO and O3 decrease but PM2.5, PM10 and NO2 increase significantly. Besides, resource intensive cities, non-key environmental protection cities and cities in the north of the region have great potential for air pollutant emission reduction. Finally, policy suggestions are recommended; these include setting specific goals at the city level, incorporating more cities into the list of key environmental protection cities, refining the concrete indicators of domestic solid fuel, and encouraging and enforcing clean heating diffusion.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Does the “Blue Sky Defense War Policy” Paint the Sky Blue?—A Case Study of Beijing–Tianjin–Hebei Region, China

Yang

Wang

Chen

et al. 2021

IJERPH

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“…The secondary industry of Tangshan is led by steel production, which accounted for 12.05% of the total steel production in China in 2021 (https://data.stats.gov.cn/). Although a series of air pollution control measures have been implemented in recent years, due to its industrial structure characterized by heavy industry and unfavorable geographical conditions, heavy pollution episodes are still inevitable, especially during the winter and spring seasons, in which buildings require heating (Guo et al 2020).…”

Section: Study Areamentioning

confidence: 99%

Analysis of factors influencing air quality in different periods during COVID- 19: A case study of Tangshan, China

Shan

Liu

et al. 2022

Preprint

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Since the outbreak of COVID-19, China has experienced the Level I, Level II, and Level III responses to the epidemic, with different measures have an impact on air quality in difference periods. This study aimed to analyze the main factors influencing air quality in three different periods during COVID-19 in Tangshan: the COVID-19 period, the Level I response period, and the Spring Festival period. Here, we used comparative analysis and difference-in-differences (DID) method to explore differences in air quality. During COVID-19, the air quality index (AQI) and the concentrations of six conventional air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h) decreased significantly compared to 2017–2019. Air quality improved may be the result of industry closures not related to livelihoods during COVID-19 in 2020. For the Level I response period, multiple linear regression (MLR) models were used to quantify the impact of COVID-19 control measures on AQI, and it was shown that the measures reduced the average AQI by 29.07%, 31.43%, and 20.04% in February, March, and April 2020, respectively. During the Spring Festival, the concentrations of the above-mentioned six pollutants were significantly higher than those in 2019 and 2021 as there were two heavy pollution episodes in 2020, and the possible pollutant sources were local emissions and short-distance transportation. As for the further improvement of air quality, it is necessary to take strict measures to prevent and control air pollution while paying attention to meteorological factors.

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“…The problem of air pollution has always been one of the major topics of universal concern in the world 1,2 . In recent years, with the rapid development of China's transportation and industry and the influence of factors such as heating and coal burning in North China, air pollution generally shows a trend of continuous deterioration [3][4][5] .…”

Section: Response Of Plant Reflectance Spectrum To Simulated Dust Depmentioning

confidence: 99%

Response of plant reflectance spectrum to simulated dust deposition and its estimation model

Zhu

Zhang

et al. 2020

Sci Rep

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To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350–2500 nm. A steep reflection platform appears in the 692–763 nm band. In 760–1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680–740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x − 1.4791, R2 = 0.7091, RMSE = 0.9725).

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Study on clean heating based on air pollution and energy consumption

Cited by 13 publications

References 25 publications

Does the “Blue Sky Defense War Policy” Paint the Sky Blue?—A Case Study of Beijing–Tianjin–Hebei Region, China

Does the “Blue Sky Defense War Policy” Paint the Sky Blue?—A Case Study of Beijing–Tianjin–Hebei Region, China

Analysis of factors influencing air quality in different periods during COVID- 19: A case study of Tangshan, China

Response of plant reflectance spectrum to simulated dust deposition and its estimation model

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