Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022

Zhang, Binqian; Zhang, Yunjiang; Zhang, Kexin; Zhang, Yichen; Ji, Yao; Zhu, Baizhen; Liang, Zeye; Wang, Hongli; Ge, Xinlei

doi:10.1016/j.scitotenv.2023.163189

Cited by 11 publications

(3 citation statements)

References 56 publications

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“…Results are improved with the suggested IVLSTM model. Zhang et al 13 examine the seasonal relationship between NO 2 , SO 2 , and CO air pollutants for six cities in the Tibet region of China by using multiple linear regression (MLR) and RF. The findings of this study show that manmade emission reductions are the primary drivers of improved PM 2.5 air quality in the Tibetan Plateau.…”

Section: Literature Reviewmentioning

confidence: 99%

“…6,[10][11][12] There are studies suggesting that numerous health issues affecting people, including lung illnesses, asthma, non-asthmatic respiratory symptoms, cardiovascular mortality, and cardiovascular morbidity, are linked to living close to roadways in urban areas, and these issues are caused by air pollution from traffic. [13][14][15] Considering the serious impact of air pollution on human health and life, the concentration estimation of air pollutants has received great attention as it can provide people with accurate information about air quality by properly planning future activities. [16][17][18][19] With precise estimation of air pollutant concentrations, decision-makers can take the required steps to reduce air pollution such as environmental pollution units could profit from an hourly forecast of particulate matter (PM) to issue further preventive action if necessary.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Taşağıl Arslan,

Kılıç Depren

2024

Journal of Chemometrics

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The rapid advancement of industrialization and urbanization has led to the global problem of air pollution. Air quality can decrease due to pollutants in the air, including types of gases and particles that are carcinogenic, causing adverse health effects. Therefore, estimating the concentration of air pollutants is of great interest as it can provide accurate information about air quality with proper planning of future activities. In this manner, this study considers Istanbul, a province with a high concentration of industry, population, and vehicle traffic. Particulate matter (PM), one of the most basic air pollutants, is stated to contain microscopic solids or liquid droplets that are small enough to be inhaled and cause serious health problems. Thus, it is recommended to apply discrete wavelet transform (DWT) and deep learning method long short‐term memory (LSTM) as a hybrid model to predict the concentration of PM10. Using the mentioned methods, they can predict air pollution to have been developed within the scope of this study. Furthermore, the hybrid approach with LSTM by selecting the most appropriate discrete wavelet type emphasizes the difference of this study from the existing literature. The ability of these developed methods to make successful future predictions helps institutions and organizations that can take precautions on the subject to take action at the right time; in addition, the deep learning methods used contribute to the development of sustainable smart environmental systems. In today's environment when air pollution is increasing and threatening human health, any precaution that can be taken would improve the quality of life for all living things, reduce health issues and deaths caused by air pollution, and thus raise the degree of well‐being. These findings might offer a reliable scientific evidence for Istanbul City's air pollution management, which can serve as an example for other regions.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Taşağıl Arslan,

Kılıç Depren

2024

Journal of Chemometrics

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“…Findings showed that PM 2.5 is mostly sensitive to NH 4 + , NO 3 − , and SO 4 2− , in that order. Zhang et al 27 quantified anthropogenic and weather drivers of PM 2.5 concentrations using Random Forest; the authors attributed an improved air quality in terms of PM 2.5 to a decrease in anthropogenic emission. The importance of weather drivers varied between locations, where zonal wind speed at 500 hpa, relative humidity, and total precipitation were among the dominant factors.…”

Section: Introductionmentioning

confidence: 99%

Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants

Gerges,

Llaguno-Munitxa,

Zondlo

et al. 2024

Environ. Sci. Technol.

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Urban air quality persists as a global concern, with critical health implications. This study employs a combination of machine learning (gradient boosting regression, GBR) and spatial analysis to better understand the key drivers behind air pollution and its prediction and mitigation strategies. Focusing on New York City as a representative urban area, we investigate the interplay between urban characteristics and weather factors, showing that urban features, including traffic-related parameters and urban morphology, emerge as crucial predictors for pollutants closely associated with vehicular emissions, such as elemental carbon (EC) and nitrogen oxides (NO x ). Conversely, pollutants with secondary formation pathways (e.g., PM 2.5 ) or stemming from nontraffic sources (e.g., sulfur dioxide, SO 2 ) are predominantly influenced by meteorological conditions, particularly wind speed and maximum daily temperature. Urban characteristics are shown to act over spatial scales of 500 × 500 m 2 , which is thus the footprint needed to effectively capture the impact of urban form, fabric, and function. Our spatial predictive model, needing only meteorological and urban inputs, achieves promising results with mean absolute errors ranging from 8 to 32% when using full-year data. Our approach also yields good performance when applied to the temporal mapping of spatial pollutant variability. Our findings highlight the interacting roles of urban characteristics and weather conditions and can inform urban planning, design, and policy.

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Impact of COVID-19 restrictions liberalization on air quality: a case study of Chongqing, Southwest China

Wang,

Zhang,

Chen

et al. 2024

Environ Monit Assess

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Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022

Cited by 11 publications

References 56 publications

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants

Impact of COVID-19 restrictions liberalization on air quality: a case study of Chongqing, Southwest China

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Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022

Cited by 11 publications

References 56 publications

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM10 concentrations

Weather and the City: Machine Learning for Predicting and Attributing Fine Scale Air Quality to Meteorological and Urban Determinants

Impact of COVID-19 restrictions liberalization on air quality: a case study of Chongqing, Southwest China

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Product

Resources

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Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations

Optimizing air quality predictions: A discrete wavelet transform and long short‐term memory approach with wavelet‐type selection for hourly PM₁₀ concentrations