AIMC Topic: Air Pollutants

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Oxygenated Organic Molecules over the Boundary Layer Aloft in Beijing.

Environmental science & technology
Oxygenated organic molecules (OOMs) originate from both direct emissions and secondary formation via the oxidation of volatile organic compounds (VOCs) emitted from biogenic and anthropogenic sources. OOMs are suggested to play a crucial role in the ...

Decoding PM oxidative potential in Ningbo, China: Key chemicals, sources, and health risks via dual-assay and machine learning.

Journal of hazardous materials
PM oxidative potential (OP), a key driver of health risks, was investigated in Ningbo, China, using dual dithiothreitol (DTT) and ascorbic acid (AA) assays combined with machine learning (ML). This approach accounts for the complexity of interactions...

Long-term exposure to PM and liver cancer mortality: Insights into the role of smaller particulate fractions.

Ecotoxicology and environmental safety
Particulate matter (PM) is a recognized carcinogen, but the effects of PM on liver cancer remain underexplored. This study investigates the long-term association between PM and liver cancer mortality, as well as the contribution of smaller particles ...

Exploring Aerosol Vertical Distributions and Their Influencing Factors: Insight from MAX-DOAS and Machine Learning.

Environmental science & technology
Understanding aerosol vertical distribution is crucial for aerosol pollution mitigation but is hindered by limited observational data. This study employed multiaxis differential optical absorption spectroscopy (MAX-DOAS) technology with a coupled rad...

Enhancing particulate matter prediction in Delhi: insights from statistical and machine learning models.

Environmental monitoring and assessment
This study advances our approach to modeling particulate matter levels-specifically, PM and PM-in Delhi's dynamic urban environment through an extensive evaluation of traditional time series models (ARIMAX, SARIMAX) and machine learning models (RF, S...

Mitigating bias induced by missing data in new-generation geostationary satellite monitoring of ground-level NO via machine learning.

Environmental pollution (Barking, Essex : 1987)
The Geostationary Environment Monitoring Spectrometer (GEMS) has revolutionized air quality monitoring with hourly resolution from geostationary Earth orbit (GEO). However, satellite-derived air quality data often face limitations and biases due to m...

Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM in China.

Frontiers in public health
OBJECTIVES: This study investigated association between long-term PM exposure and lung cancer incidence, focusing on Jiangsu Province, China. We aimed to explore the effects of historical PM with time lags and build a prediction model using machine l...

Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study.

BMC public health
BACKGROUND: Environmental factors have been identified as significant risk factors for scrub typhus. However, the impact of inorganic compounds such as greenhouse gases and air pollutants on the incidence of scrub typhus has not been evaluated.

Satellite data to support air quality assessment and management.

Journal of the Air & Waste Management Association (1995)
Satellite data have long been recognized as valuable for air quality applications. These applications are in a stage of rapid growth: new geostationary satellites provide hourly or sub-hourly data; improvements in algorithms convert measured waveleng...