PM2.5 and O3 pollution during 2015–2019 over 367 Chinese cities: Spatiotemporal variations, meteorological and topographical impacts

Zhao, Suping; Yin, Daiying; Yu, Ye; Kang, Shichang; Qin, Dahe; Dong, Lijun

doi:10.1016/j.envpol.2020.114694

Cited by 148 publications

(71 citation statements)

References 60 publications

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“…As a result, the near-surface atmospheric O 3 concentrations and the number of days exceeding the standard have shown a significant upward trend in the past three years (Cheng et al, 2017;Zhang and Zhang, 2019). Therefore, factors of influencing O 3 formation and spatiotemporal distribution of O 3 concentration need to be better understood to take measures for O 3 control (Cheng et al, 2018;Yang et al, 2020;Zhao et al, 2020). However, the spatiotemporal dynamics and influencing factors of near-surface O 3 concentration in Beijing are not clear.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Changes of Near-Surface Ozone Concentration From 2015 to 2018 in Beijing

Xie¹,

Shi

et al. 2021

Front. Environ. Sci.

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In this study, we investigated the spatiotemporal changes of near-surface O3 concentration based on 33 automatic air quality monitoring stations in Beijing in 2015 and 2018. Here we show that the diurnal variations for O3 concentration at 33 monitoring stations exhibited a single-peak mode with a minimum concentration from 06:00 to 07:00 and a maximum value from 14:00 to 15:00. We find that the O3 concentration was significantly positively correlated with the air temperature, solar total radiation, and wind speed, while it was negatively correlated with the relative humidity. Among those factors, the air temperature played the most important role in influencing O3 concentration (relative contribution is greater than 88% in the boosted regression trees model). We also present that the annual variation of O3 concentration at all stations tended to be a “bell-shaped” curve distribution with a peak in summer and the lowest value in winter. The annual averaged O3 concentration at 33 stations in Beijing was 57.5 ± 9.8 μg⋅m−3 in 2015 and 60.3 ± 9.0 μg⋅m−3 in 2018. In addition, we detect that the annual averaged value of the O3 concentration was lower in the central and southern Beijing, and higher in the northern Beijing both in 2015 and 2018. The spatial difference of the O3 concentration could be explained by the traffic pollution, vegetation coverage, atmospheric regional transmission, and atmospheric particulate matter concentration.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Changes of Near-Surface Ozone Concentration From 2015 to 2018 in Beijing

Xie¹,

Shi

et al. 2021

Front. Environ. Sci.

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“…The tapered element oscillating microbalance or b-absorption methods were used to measure PM 2.5 concentration, and UV-spectrophotometry was used to measure O 3 concentration. Air quality measurements, data quality control procedures, uncertainties, and accuracies are described in the China Environmental Protection Standards such as " HJ 193-2013" and"HJ 655-2013" (Ministry of Environmental Protection (MEP) of the People's Republic of China, 2013aChina, , 2013b and the Chinese National Standard HJ/T 193e2005 (Xie et al, 2016;Zhang & Cao, 2015;Zhao et al, 2020 FNL grid. The near-surface meteorological variables in FNL are further bias-corrected based on the collected near-surface observations.…”

Section: Methodsmentioning

confidence: 99%

Impacts of Extreme Air Pollution Meteorology on Air Quality in China

Yang

Shao

2021

JGR Atmospheres

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Extreme air pollution meteorological events (EAPMEs), such as atmospheric stagnation (AS), heat wave (HW), and temperature inversion, significantly affect regional air quality. In the present study, considerably increased EAPME frequency with clear spatial variations is identified in China from 1960 to 2019. Strong air quality sensitivities to EAPMEs are identified by combing air quality and meteorological observations. We found that HW greatly affects ozone concentration and may enhance the generation of fine particles. AS showed a noticeable but opposite impact on fine particle pollution between northern and southern China owing to the wintertime heating policy. EAPMEs may increase the probability of air pollution for pollution levels greater than 2. For example, the synthetic effects of multiple EAPMEs could increase the probability of summer ozone and winter fine particle pollution in China by factors of up to 5 and 2, respectively. Based on such information, more attention may be needed on the collaborative prediction of extreme meteorological events and air pollution episodes.

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“…The constraint of energy usage intensity is that it does not consider elements that include energy utilization for HVAC and lightings' systems efficiency individually [6]. Energy endorsement is used as an auxiliary to regulate energy usage intensity to measure the energy performance of HVAC and lightings' system with various operational conditions and operational modes [7]. Energy endorsement models are depleted for a long time in order to calculate and control buildings' energy performance in different climatic conditions.…”

Section: Energy Savings and Energy Indexmentioning

confidence: 99%

“…Smart buildings application delivers a possibility of minimizing the electrical energy in a way that it will direct to substantial reduction in CO 2 emissions. The environmental evaluation of the proposed control system depends on the avoided amount of CO 2 emissions caused by electricity savings which can be expressed using (7). [21]- [23].…”

Section: Payback Period Estimationmentioning

confidence: 99%

Energy, economic and environmental analysis of fuzzy logic controllers used in smart buildings

Baniyounes

Ghadi²,

Zahia

et al. 2021

IJPEDS

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This article is divided into three parts: the first presents a simulation study of the effect of occupancy level on energy usage pattern of Engineering building of Applied Science Private university, Amman, Jordan. The simulation was created on simulation mechanism by means of EnergyPlus software and improved by using the building’s data such as building’s as built plan, occupant’s density level based on data about who utilize the building throughout operational hours, energy usage level, Heating Ventilating and air conditioning (HVAC) system, lighting and its control systems and etc. Data regarding occupancy density level estimation is used to provide the proposed controller with random number of users grounded on report were arranged by the university’s facilities operational team. The other division of this paper shows the estimated saved energy by the means of suggested advanced add-on, FUZZY-PID controlling system. The energy savings were divided into summer savings and winter savings. The third division presents economic and environmental analysis of the proposed advanced fuzzy logic controllers of smart buildings in Subtropical Jordan. The economic parameters that were used to evaluate the system economy performance are life-cycle analysis, present worth factor and system payback period. The system economic analysis was done using MATLAB software

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PM2.5 and O3 pollution during 2015–2019 over 367 Chinese cities: Spatiotemporal variations, meteorological and topographical impacts

Cited by 148 publications

References 60 publications

Spatiotemporal Changes of Near-Surface Ozone Concentration From 2015 to 2018 in Beijing

Spatiotemporal Changes of Near-Surface Ozone Concentration From 2015 to 2018 in Beijing

Impacts of Extreme Air Pollution Meteorology on Air Quality in China

Energy, economic and environmental analysis of fuzzy logic controllers used in smart buildings

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