[Remote Sensing Model for Estimating Atmospheric PM2.5 Concentration in the Guangdong-Hong Kong-Macao Greater Bay Area].

PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 µg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 µg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 µg·m-3 to 112.780 µg·m-3 and the lowest values ranging from 5.000 µg·m-3 to 7.899 µg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 µg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area.

[Hyperspectral remote sensing of chlorophyll a concentrations in the Lake Taihu, based on water optical classification].

Four field investigations into the Lake Taihu were carried out for collecting in situ observed data in Nov., 2006, Nov., 2008, May and Aug. , 2010. On the basis of water optical classification, different retrieval algorithms were developed, specifically for different types of waters. The obtained optimal models were (1) the four-band model for Type 1 water; (2) the first-order differential model for Type 2 and Type 3 waters. Meanwhile, an optimal retrieval model was also established using the same aggregated calibration data. Some comparisons were done between the developed models for the classified and non-classified waters. The compared results showed that models for the classified waters had better performances than that for the non-classified water, in both retrieval accuracy and model stability. The findings of this study are significant for promoting the development of water color remote sensing for optically complex turbid inland waters.

[Hyperspectral remote sensing of total suspended matter concentrations in Lake Taihu based on water optical classification].

Four field investigations were carried out in the Taihu Lake for collecting in situ observation data in Nov. 2008, Apr. 2009, May and Aug. 2010. On the basis of water optical classification, different retrieval algorithms were developed, specific for different types of waters. Based on the preformance of each model comparion in each type of waters, the optimal models obtained were (1) the band ratio model for Type 1 water; (2) the semi-analysis algorithm model 2 for Type 2 water; (3) the first-order differential model for Type 3 water. Meanwhile, an optimal retrieval model was also established using the same collection of calibration data. Some comparisons were done between the developed models for the classified and non-classified waters. The comparison results showed that the models for the classified waters had better performances than that for the non-classified water, in both the retrieval accuracy and the model stability. Then, analyses of the optical classification leading to the accuracy decrease of the semi-analysis algorithm model were processed. Finally, the results of this study in hyperspectral remote sensing date showed a great application potential by analysis. The findings of this study are significant for promoting the development of water color remote sensing for optically complex turbid inland waters.

Specific inherent optical quantities of complex turbid inland waters, from the perspective of water classification.

For optically complex turbid productive waters, the optical behavior of suspended particles is the keynote of characterizing the unordered variations of inherent optical properties (IOPs). Multiple bio-optical measurements and sampling of optically active substances were performed in Lake Taihu, Lake Chaohu, and Lake Dianchi, and Three Gorges reservoir of China, in 2008, 2009, and 2010. On the basis of obtaining adequate observation data, we developed an improved and robust water classification approach, by which complex water conditions were divided into three types, i.e., Type 1 (Normalized Trough Depth at 675 nm, hereafter NTD675, ≥0.092), Type 2 (0 < NTD675 < 0.092), and Type 3 (NTD675 ≤ 0). Furthermore, the specific inherent optical quantities for suspended particles, including the specific absorption coefficient of non-algal particles (a*(nap)), the specific absorption coefficient of phytoplankton (a*(ph)), and the specific scattering coefficient of the suspended particles (b*(p)), were determined for the three classified types of waters. The validation results showed that our proposed values for these specific inherent optical quantities presented relatively high predictive accuracies, with most mean absolute percentage errors (MAPE) near 30%, and more importantly, performed much better than that of non-classified waters. Additionally, relative contributions of phytoplankton and non-algal particles to the total particulate absorption and scattering, as well as the spectra, were also analyzed, and the differences among the three classified types of waters were clarified. Overall, the results obtained in this study provide us with new knowledge for understanding complex varied inherent optical properties of highly turbid productive waters.

[Preliminary exploring of hyperspectral remote sensing experiment for nitrogen and phosphorus in water].

The content of nitrogen and phosphorus in the waters is an important index to measure water quality, and the technique of remote sensing plays a large role in monitoring the change in environment. The reflectance spectra of nitrogen and phosphorus with different concentrations were measured to discover their special features under pure water condition in the laboratory by hyperspectral remote sensing technique. The result shows that nitrogen has reflectance peaks at 404 and 477 nm, and phosphorus at 350 nm, and these reflectance peaks have a good correlation with their concentrations, then a quantitative retrieval model was deduced for nitrogen and phosphorus based on that. These results will lay an important basis for further monitoring nitrogen and phosphorus by remote sensing technique in the big inland lakes, reservoirs and rivers.

[Scattering characteristics of Taihu Lake and its relationship models with suspended particle concentration].

Water scattering characteristics are closely related to water quality parameters, such as suspended particles and their concentrations. Through the observing system of water inherent optical properties, which were developed by WETlabs Inc, the backscattering and scattering coefficients of Lake Taihu had been obtained in Oct. 2006 and Nov. 2006. Based on analysis of data, the backscattering coefficient spectra model had been established. In addition, the water refraction indexes were computed by backscattering ratio. According to the change scopes of refraction index, the dominant factors of in-water particles were divided into three categories: (1) phytoplankton; (2) inorganic particles; (3) both of the above. By analyzing the correlations between scattering coefficients with inorganic particle, organic particle and total particle concentration, the relationship between scattering coefficient and inorganic particle concentration was simulated well by power function for three different categories respectively.

[Integrated fuzzy evaluation of water eutrophication based on GIS in the Taihu Lake].

Supported by geographic information system and geostatistics, the application of fuzzy mathematics and analytic hierarchy process for water eutrophication evaluation was discussed. Taking Taihu Lake as an example, the research selected total phosphorus, total nitrogen, chlorophyll a, COD, BOD5, DO and transparence as evaluation index. After geostatistical analysis of the datum of monitoring site, the values of evaluation indices were estimated in the whole research area. Given that, the different dependence functions were developed for these indices and the function values were calculated. Furthermore, according to the principle of analytic hierarchy process, the weight of every index was calculated, then integrated evaluation value was obtained for the whole research area and the evaluation map for water eutrophication was drawn. The result shows that the level of nutrition is the highest in the north and north-west of lake, which is hypertrophic, that is meso-eutrophic in the middle and that is the lowest in the south-east of lake, which is mesotrophic.