Application of machine learning (individual vs stacking) models on MERRA-2 data to predict surface PM2.5 concentrations over India

Long-term spatiotemporal variation of benzo[a]pyrene in Japan: Significant decrease in ambient concentrations, human exposure, and health risk

Although Benzo[a]pyrene (BaP) is considered carcinogenic to humans, the health effects of exposure to ambient levels have not been sufficiently investigated. This study estimated the long-term spatiotemporal variation of BaP in Japan over nearly two decades at a fine spatial resolution of 1 km. This study aimed to obtain an accurate spatiotemporal distribution of BaP that can be used in epidemiological studies on the health effects of ambient BaP exposure. The annual BaP concentrations were estimated using an ensemble machine learning approach using various predictors, including the concentrations and emission intensities of the criteria air pollutants, and meteorological, land use, and traffic-related variables. The model performance, evaluated by location-based cross-validation, exhibited satisfactory accuracy (R2 of 0.693). Densely populated areas showed higher BaP levels and greater temporal reduction, whereas BaP levels remained higher in some industrial areas. The population-weighted BaP in 2018 was 0.12 ng m-3, a decrease of approximately 70% from its 2000 value of 0.44 ng m-3, which was also reflected in the estimated excess number of lung cancer incidences. Accordingly, the proportion of BaP exposure below 0.12 ng m-3, which is the BaP concentration associated with an excess lifetime cancer risk of 10-5, reached 67% in 2018. Our estimates can be used in epidemiological studies to assess the health effects of BaP exposure at ambient concentrations.

The drug loading capacity prediction and cytotoxicity analysis of metal-organic frameworks using stacking algorithms of machine learning

Metal-organic frameworks (MOFs) have shown excellent performance in the field of drug delivery. Despite the synthesis of a vast array of MOFs exceeding 100,000 varieties, certain formulations have exhibited suboptimal performance characteristics. Therefore, there is a pressing need to enhance their efficacy by identifying MOFs with superior drug loading capacities and minimal cytotoxicity, which can be achieved through machine learning (ML). In this study, a stacking regression model was developed to predict drug loading capacity and cytotoxicity of MOFs using datasets compiled from various literature sources. The model exhibited exceptional predictive capabilities, achieving R2 values of 0.907 for drug loading capacity and 0.856 for cytotoxicity. Furthermore, various model interpretation methods including partial dependence plots, individual conditional expectation, Shapley additive explanation, decision tree, random forest, CatBoost Regressor, and light gradient-boosting machine were employed for feature importance analysis. The results revealed that specific metal atoms such as Zn, Cr, Fe, Zr, and Cu significantly influenced the drug loading capacity and cytotoxicity of MOFs. Through model validation encompassing experimental validation and computational verification, the reliability of the model was thoroughly established. In general, it is a good practice to use ML methods for predicting drug loading capacity and cytotoxicity analysis of MOFs, guiding the development of future property prediction methods for MOFs.

Characterization of microplastics in outdoor and indoor air in Ranchi, Jharkhand, India: First insights from the region

The study focused on detecting and characterizing microplastics in outdoor and indoor air in Ranchi, Jharkhand, India during post-monsoon (2022) and winter (2023). Stereo microscopic analysis showed that plastic fibres had a dominant presence, fragments were less abundant, whereas fewer films could be detected in indoor and outdoor air. The atmospheric deposition of microplastics outdoors observed 465 ± 27 particles/m2/day in PM10 and 12104 ± 665 and 13833 ± 1152 particles/m2/day in PM2.5 in quartz and PTFE, respectively during the post-monsoon months. During winter, microplastic deposition rates in PM10 samples were found to be 689 ± 52 particles/m2/day and 19789 ± 2957 and 30087 ± 13402 in quartz and PTFE particles/m2/day respectively in PM2.5. The mean deposition rate in indoor environment during post-monsoon was 8.3 × 104 and 1.03 × 105 particles/m2/day in winter. During the post-monsoon period in PM10, there were fibres from 7.7 to 40 μm and fragments from 2.3 μm to 8.6 μm. Indoor atmospheric microplastics, fibres ranged from 1.2 to 47 μm and fragments from 0.9 to 16 μm present respectively during the post-monsoon season. Fibres and fragment sizes witnessed during winter were 3.6-6.9 μm and 2.3-34 μm, respectively. Indoor air films measured in the range of 4.1-9.6 μm. Fourier transform infrared analysis showed that outdoor air contained polyethylene, polypropylene, Polystyrene, whereas indoor air had polyvinyl chloride. Polyethylene mainly was present in outdoor air, with lesser polypropylene and polystyrene than indoors, where polyvinyl chloride and polyethylene were in dominant proportions. Elemental mapping of outdoor and indoor air samples showed the presence of elements on the microplastics. The HYSPLIT models suggest that the particles predominantly were coming from North-West during the post-monsoon season. Principal component analysis indicated wind speed and direction influencing the abundance of microplastics. Microplastics concentration showed strong seasonal influence and potential to act as reservoir of contaminants.

Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale

Aerosol Atmospheric Rivers (AARs) are elongated and narrow regions that carry high concentrations of aerosols (tiny particles suspended in the atmosphere) across large distances, exerting effects on both air quality and human health (Chakraborty et al., 2021, 2022). Monitoring and modeling these aerosols present distinct challenges due to their dynamic nature and complex interactions within the atmosphere. In this context, the present study detects and predicts the AARs using MERRA-2 reanalysis datasets with their seasonal climatology of key aerosol species, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphates (SU). The study employs an innovative Integrated Aerosol Transport (IAT) based AAR algorithm from 2015 to 2022. A total count of 44,020 BC AARs, 13,280 DU AARs, 21,599 OC AARs, 17,925 SS AARs, and 31,437 SU AARs were detected globally. The seasonal climatology of BC and OC AARs intensifies in areas such as the Amazon rainforest and Congo during AMJJAS (April-September) due to forest fires. Similarly, DU AARs are more frequent in regions near the Saharan desert, primarily around the equator during AMJJAS. SS AARs tend to predominate over the oceans, while SU AARs are predominantly found in the northern hemisphere, primarily due to higher anthropogenic emissions. Furthermore, convolutional autoencoder-based models were developed for key aerosol species, strengthening predictive accuracy by effectively capturing complex data relationships and delivering precise predictions for the last 5-time frames. During validation, the model evaluation parameters for image prediction such as the Structural Similarity Index ranged from 0.86 to 0.94, Peak Signal-to-Noise Ratio fluctuated between 1.14 and 42.25 dB, Root Mean Square Error varied from 2.39 to 296.4 mg/(m-sec), and Mean Square Error fell within the range of 1.55-17.22 mg/(m-sec). These collectively reflect image closeness, quality, dissimilarity, and accuracy in AAR prediction. This study demonstrates the effectiveness of advanced machine and deep learning models in predicting AARs, offering the potential for advanced forecasting and enhancing resilience in high-aerosol concentration regions.