Concentration and source allocation of black carbon by AE-33 model in urban area of Shenzhen, southern China.

Purpose: In the urban region of Shenzhen, the changes in the concentration of Black carbon (BC) have been evaluated throughout the dry season, and apportioned the BC sources, including in the form of fossil fuel (e.g., vehicle emissions) and biomass fuel (e.g., industrial emissions). Methods: The new seven-channel aethalometer model (AE-33), PM2.5, and meteorological data were collected in the dry season (October-May) from 2019 to 2020, to quantify BC emissions in urban Shenzhen. Explored the source allocation of BC based on Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) model. Results: We revealed that the mean BC concentration was 2672 ± 1506 ng/m3 in the dry season, with values of 4062 ± 1182 ng/m3, 2519 ± 1568 ng/m3, and 1900 ± 776 ng/m3 in autumn, winter, and spring, respectively. Additionally, we found that fossil fuels have higher contributions to BC concentrations (86.3% to 86.8% in autumn and spring) in the dry season than biomass fuels (16% to 20% in autumn, spring and winter), which is different from Beijing, Nanjing and other large economic zones in China. The diurnal variation in BC and the contribution of fossil fuels indicate that there is a significantly greater increase in BC during peak traffic hours in urban Shenzhen than in other cities. Finally, meteorological parameters and PM2.5 data provided supporting evidence that BC is sourced mainly from local vehicle emissions and industry-related combustion in the western and northeastern/southeastern parts of the study area. Conclusion: This study showed that the concentration of BC is lower than other regions, and the source allocation is mainly local fossil fuels (vehicle emission, etc.). Supplementary Information: The online version contains supplementary material available at 10.1007/s40201-022-00793-3.

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing.

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R2 = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.

Estimating PM2.5 with high-resolution 1-km AOD data and an improved machine learning model over Shenzhen, China.

Studies on fine particulate matter with an aerodynamic diameter of 2.5 µm or smaller (PM2.5) are closely related to the atmospheric environment and human activities but are often limited by ground-level in situ observations. Satellite remote sensing techniques have been widely used to estimate the PM2.5 concentration over large areas where ground-monitoring sites are unavailable. However, satellite-retrieved aerosol optical depth (AOD) products usually feature a coarse resolution, which is insufficient for the estimation of the urban-scale PM2.5 concentration. We developed a new improved random forest (IRF) model based on machine learning and a newly released AOD product with a high resolution of 1-km, which could more effectively and accurately estimate the PM2.5 concentration over Shenzhen in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China. Daily PM2.5 concentrations from 2016 to 2018 were estimated from ground-level PM2.5 and meteorological variable data. The popular linear regression model, geographically and temporally weighted regression (GTWR) model and random forest (RF) model without spatiotemporal information were employed for comparison and validation purposes through the 10-fold cross-validation (CV) approach. The IRF model attained an overall R2 value of 0.915 and a root mean square error (RMSE) value of 3.66 µg m-3. This suggests that the IRF model can estimate the urban PM2.5 concentration with a high spatial resolution at the daily, seasonal and annual scales, and the improved machine learning method is better than the linear model proposed by previous studies in terms of the estimation accuracy of the PM2.5 concentration. Generally, the IRF model coupled with AOD data with a 1-km resolution can significantly improve the calculation accuracy of the atmospheric PM2.5 concentration over coastal urban areas in the future.