Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA

Du, Jian; Qiao, Fengxiang; Yu, Lei

doi:10.1016/j.resconrec.2019.04.024

Cited by 38 publications

(26 citation statements)

References 40 publications

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“…The average concentration of traffic‐related PM 2.5 at participants’ residential addresses was 0.76 µg/m 3 in our study area. Compared with PM 2.5 concentrations reported in other studies in the same study area, the modeled traffic‐related PM 2.5 concentrations accounted for only 7% to 10% of total concentrations of PM 2.5 but accounted for 71% of primary PM 2.5 emissions . If comparing the average traffic‐related PM 2.5 in our study area with the near‐road gradients of traffic‐related PM 2.5 estimated in our previous study , the 0.76 µg/m 3 was close to the average concentrations at a 50‐m distance from a major road.…”

Section: Discussionsupporting

confidence: 70%

Population‐Based Study of Traffic‐Related Air Pollution and Obesity in Mexican Americans

Zhang

Zhao

Chow

et al. 2019

Obesity

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► Women are suspected of having a higher risk of obesity compared to men when exposed to the same level of exposure to trafficrelated air pollution.Objective: The purpose of this study was to assess the crosssectional association between residential exposure to trafficrelated air pollution and obesity in Mexican American adults.Methods: A total of 7,826 selfreported Mexican Americans aged 20 to 60 years old were selected from the baseline survey of the MD Anderson ManoaMano Mexican American Cohort. Concentrations of traffic related particulate matter with aerodynamic diameter < 2.5 μm were modeled at geocoded residential addresses using a dispersion models.The residential proximity to the nearest major road was calculated using a Geographic Information System. Linear and logistic regression models were used to estimate the adjusted associations between exposure and obesity, defined as BMI ≥ 30. Results: More than half (53.6%) of the study participants had BMI ≥ 30, with a higher prevalence in women (55.0%) than in men (48.8%). Overall higher trafficrelated air pollution exposures were associated with lower BMI in men but higher BMI in women. By stratifying for those who lived in a 0 to 1,500m road buffer, the oneinterquartilerange (685.1 m) increase of distance to a major road had a significant association with a 0.58kg/m 2 lower BMI (95% CI: −0.92 to −0.24) in women.Conclusions: Exposure to intensive traffic is associated with increased risk of obesity in Mexican American women.Obesity (2020) 28, 412-420.

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Section: Discussionsupporting

confidence: 70%

Population‐Based Study of Traffic‐Related Air Pollution and Obesity in Mexican Americans

Zhang

Zhao

Chow

et al. 2019

Obesity

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“…Meanwhile, PM 2.5 modeling techniques vary according to the different predictors applied and objectives. In general, there are four major categories for modeling PM 2.5 : (1) Time series analysis and related statistical analysis (e.g., [16,[51][52][53][54][55]); (2) GTWR and its derivative models (e.g., [30,[56][57][58]); (3) machine learning method using plenty of predictors (e.g., [48,49]); (4) comprehensive approach by integrating several above methods (e.g., [46]). Each method has pros and cons.…”

Section: Discussionmentioning

confidence: 99%

Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018

Wei

Zhang

Duan

et al. 2019

IJERPH

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Objective: This study investigated the relationships between PM2.5 and 5 criteria air pollutants (SO2, NO2, PM10, CO, and O3) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R2 and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R2 (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial–temporal heterogeneity and possible solutions for modeling the relationships between PM2.5 and 5 criteria air pollutants for Heilongjiang province, China.

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“…Because of the 2020 COVID-19 epidemic, human beings have become more aware of their relationship with nature and of the importance of sustaining a harmonious coexistence of man and nature. In a time of significant crises, including the COVID-19 epidemic and climate change, the international community agrees that only through the development and implementation of green and low-carbon technologies, society can achieve high-quality economic recovery [1][2][3]. On Sept. 22, during the General Debate of the 75th Session of the UNGA, Chinese President Xi Jinping declared that China aims to reach CO 2 emissions peak before 2030 and achieve carbon neutrality before 2060.…”

Section: Introductionmentioning

confidence: 99%

Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota

Peng

Yan

et al. 2021

Complexity

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China declared a long-term commitment at the United Nations General Assembly (UNGA) in 2020 to reduce CO2 emissions. This announcement has been described by Reuters as “the most important climate change commitment in years.” The allocation of China’s provincial CO2 emission quotas (hereafter referred to as quotas) is crucial for building a unified national carbon market, which is an important policy tool necessary to achieve carbon emissions reduction. In the present research, we used historical quota data of China’s carbon emission trading policy pilot areas from 2014 to 2017 to identify alternative features of corporate CO2 emissions and build a backpropagation neural network model (BP) to train the benchmark model. Later, we used the model to calculate the quotas for other regions, provided they implement the carbon emission trading policy. Finally, we added up the quotas to obtain the total national quota. Additionally, considering the perspective of carbon emission terminal, a new characteristic system of quota allocation was proposed in order to retrain BP including the following three aspects: enterprise production, household consumption, and regional environment. The results of the benchmark model and the new models were compared. This feature system not only builds a reasonable quota-related indicator framework but also perfectly matches China’s existing “bottom-up” total control quota approach. Compared with the previous literature, the present report proposes a quota allocation feature system closer to China’s policy and trains BP to obtain reasonable feature weights. The model is very important for the establishment of a unified national carbon emission trading market and the determination of regional quotas in China.

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Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA

Cited by 38 publications

References 40 publications

Population‐Based Study of Traffic‐Related Air Pollution and Obesity in Mexican Americans

Population‐Based Study of Traffic‐Related Air Pollution and Obesity in Mexican Americans

Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018

Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota

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Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA

Cited by 38 publications

References 40 publications

Population‐Based Study of Traffic‐Related Air Pollution and Obesity in Mexican Americans

Population‐Based Study of Traffic‐Related Air Pollution and Obesity in Mexican Americans

Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018

Use of BP Neural Networks to Determine China’s Regional CO2 Emission Quota

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Use of BP Neural Networks to Determine China’s Regional CO₂ Emission Quota