Dynamics of limnological parameters in reservoirs: A case study in South Brazil using remote sensing and meteorological data

Evaluation of Regression Analysis and Neural Networks to Predict Total Suspended Solids in Water Bodies from Unmanned Aerial Vehicle Images

The concentration of suspended solids in water is one of the quality parameters that can be recovered using remote sensing data. This paper investigates the data obtained using a sensor coupled to an unmanned aerial vehicle (UAV) in order to estimate the concentration of suspended solids in a lake in southern Brazil based on the relation of spectral images and limnological data. The water samples underwent laboratory analysis to determine the concentration of total suspended solids (TSS). The images obtained using the UAV were orthorectified and georeferenced so that the values referring to the near, green, and blue infrared channels were collected at each sampling point to relate with the laboratory data. The prediction of the TSS concentration was performed using regression analysis and artificial neural networks. The obtained results were important for two main reasons. First, although regression methods have been used in remote sensing applications, they may not be adequate to capture the linear and/or non-linear relationships of interest. Second, results show that the integration of UAV in the mapping of water bodies together with the application of neural networks in the data analysis is a promising approach to predict TSS as well as their temporal and spatial variations.

Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution

Characteristics of the spatial and temporal distribution of air pollutants may reveal the cause of air pollution, especially for large regions where the anthropogenic pollutant emission is concentrated. This study addresses this issue by focusing on Shandong province, which has the highest air pollutant emissions in China. First, the spatial and temporal variation characteristics of the observed concentrations of conventional pollutants are analyzed in detail. The most prominent indicator of the problem (PM_2.5), was selected as the key analytical object. On the spatial scale, the Multivariate Moran model was used to identify factors affecting the spatial distribution of PM_2.5. On the time scale, wavelet analysis was used to explore the fluctuation characteristics of PM_2.5 at different time periods. Results show that there are significant regional differences in pollutant concentration within Shandong province. The concentration of particulate matter and gaseous pollutants in western and northern Shandong is significantly higher than eastern Shandong. The average concentrations of PM_2.5, PM₁₀, SO₂ and NO₂ were highest in winter and lowest in summer, whereas concentration of O₃ peaked in summer. For PM_2.5, the annual mean concentration has a significant spatial correlation with SO₂ emission, GDP per capita, population density and energy consumption per unit of GDP; in addition, the correlation between different regions and various indices is different. On the time scale, the fluctuation energy of PM_2.5 concentrated in Dezhou and Liaocheng is the strongest on December 18 and 19, 2015. The inversion temperature has a strong influence on the daily variation of PM_2.5 concentration. The formation and evolution of atmospheric pollution, therefore, can be explored by combining the temporal and spatial distribution of pollutants, providing a comprehensive analytical method for atmospheric pollution in different regions.

Exploring the Influence Mechanism of Meteorological Conditions on the Concentration of Suspended Solids and Chlorophyll-a in Large Estuaries Based on MODIS Imagery

In estuary areas, meteorological conditions have become unstable under the continuous effects of climate change, and the ecological backgrounds of such areas have strongly been influenced by anthropic activities. Consequently, the water quality of these areas is obviously affected. In this research, we identified periods of fluctuation of the general meteorological conditions in the Yangtze River Estuary using a wavelet analysis. Additionally, we performed a spatiotemporal evaluation of the water quality in the fluctuating period by using remote sensing modeling. Then, we explored how the fluctuating meteorological factors affect the distribution of total suspended solids (TSS) and chlorophyll-a (Chla) concentration. (1) The results show that from 2000 to 2015, temperature did not present significant fluctuations, while wind speed (WS) and precipitation (PR) presented the same fluctuation period from January 2012 to December 2012. (2) Based on the measured water sample data associated with Moderate Resolution Imaging Spectroradiometer (MODIS) imagery, we developed a water quality algorithm and depicted the TSS and Chla concentrations within the WS and PR fluctuating period. (3) We found that the TSS concentration decreased with distance from the shore, while the Chla concentration showed an initially decreasing trend followed by an increasing trend; moreover, these two water quality parameters presented different inter-annual variations. Then, we discussed the correlation between the changes in the TSS and Chla concentrations and the WS and PR variables. The contribution of this research is reflected in two aspects: 1. variations in water quality parameters over a wide range of water bodies can be evaluated based on MODIS data; 2. data from different time periods showed that the fluctuations of meteorological elements had different impacts on water bodies based on the distance from the shore. The results provide new insights for the management of estuary water environments.