High-Resolution PM2.5 Estimation Based on the Distributed Perception Deep Neural Network Model

The accurate measurement of the PM2.5 individual exposure level is a key issue in the study of health effects. However, the lack of historical data and the minute coverage of ground monitoring points are obstacles to the study of such issues. Based on the aerosol optical depth provided by NASA, combined with ground monitoring data and meteorological data, it is an effective method to estimate the near-ground concentration of PM2.5. With the deepening of related research, the models used have developed from univariate and multivariate linear models to nonlinear models such as support vector machine, random forest model, and deep learning neural network model. Among them, the depth neural network model has better performance. However, in the existing research, the variables used are input into the same neural network together, that is, the complex relationship caused by the lag effect of features and the correlation and partial correlation between features have not been considered. The above neural network framework can not be well applied to the complex situation of atmospheric systems and the estimation accuracy of the model needs to be improved. This is the first problem that we need to be overcome. Secondly, in the missing data value processing, the existing studies mostly use single interpolation methods such as linear fitting and Kriging interpolation. However, because the time and place of data missing are complex and changeable, a single method is difficult to deal with a large area of strip and block missing data. Moreover, the linear fitting method is easy to smooth out the special data in bad weather. This is the second problem that we need to overcome. Therefore, we construct a distributed perception deep neural network model (DP-DNN) and spatiotemporal multiview interpolation module to overcome problems 1 and 2. In empirical research, based on the Beijing–Tianjin–Hebei–Shandong region in 2018, we introduce 50 features such as meteorology, NDVI, spatial-temporal feature to analyze the relationship between AOD and PM2.5, and test the performance of DP-DNN and spatiotemporal multiview interpolation module. The results show that after applying the spatiotemporal multiview interpolation module, the average proportion of missing data decreases from 52.1% to 4.84%, and the relative error of the results is 27.5%. Compared with the single interpolation method, this module has obvious advantages in accuracy and level of completion. The mean absolute error, relative error, mean square error, and root mean square error of DP-DNN in time prediction are 17.7 μg/m3, 46.8%, 766.2 g2/m6, and 26.9 μg/m3, respectively, and in space prediction, they are 16.6 μg/m3, 41.8%, 691.5 μg2/m6, and 26.6 μg/m3. DP-DNN has higher accuracy and generalization ability. At the same time, the estimation method in this paper can estimate the PM2.5 of the selected longitude and latitude, which can effectively solve the problem of insufficient coverage of China’s meteorological environmental quality monitoring stations.

Determination of Riparian Vegetation Biomass from an Unmanned Aerial Vehicle (UAV)

The need to rely on accurate information about the wood biomass available in riparian zones under management, inspired the land reclamation authority of southern Tuscany to develop a research based on the new remote sensing technologies. With this aim, a series of unmanned aerial vehicle (UAV) flight campaigns flanked by ground-data collection were carried out on 5 zones and 15 stream reaches belonging to 3 rivers and 7 creeks, being representative of the whole area under treatment, characterized by a heterogeneous spatial distribution of trees and shrubs of different sizes and ages, whose species’ mix is typical of this climatic belt. A careful preliminary analysis of the zones under investigation, based on the available local orthophotos, followed by a quick pilot inspection of the riverbank segments selected for trials, was crucial for choosing the test sites. The analysis of a dataset composed of both measured and remotely sensed acquired parameters allowed a system of four allometric models to be built for estimating the trees’ biomass. All four developed models showed good results, with the highest correlation found in the fourth model (Model 4, R2 = 0.63), which also presented the lowest RMSE (0.09 Mg). The biomass values calculated with Model 4 were in line with those provided by the land reclamation authority for selective thinning, ranging from 38.9 to 70.9 Mg ha−1. Conversely, Model 2 widely overestimated the actual data, while Model 1 and Model 3 offered intermediate results. The proposed methodology based on these new technologies enabled an accurate estimation of the wood biomass in a riverbank environment, overcoming the limits of a traditional ground monitoring and improving management strategies to benefit the river system and its ecosystems.

Study on Collaborative Emission Reduction in Green-House and Pollutant Gas Due to COVID-19 Lockdown in China

In recent years, as China’s peaking carbon dioxide emissions and air pollution control projects have converged, scholars have begun to focus on the synergistic mechanisms of greenhouse gas and pollution gas reduction. In 2020, the unprecedented coronavirus pandemic, which led to severe nationwide blockade measures, unexpectedly provided a valuable opportunity to study the synergistic reduction in greenhouse gases and polluting gases. This paper uses a combination of NO2, O3, and CO2 column concentration products from different satellites and surface concentrations from ground-based stations to investigate potential correlations between these monitoring indicators in four Chinese representative cities. We found that XCO2 decreased in March to varying degrees in different cities. It was witnessed that the largest decrease in CO2, −1.12 ppm, occurred in Wuhan, i.e., the first epicenter of COVID-19. We also analyzed the effects of NO2 and O3 concentrations on changes in XCO2. First, in 2020, we used a top-down approach to obtain the conclusion that the change amplitude of NO2 concentration in Beijing, Shanghai, Guangzhou, and Wuhan were −24%, −18%, −4%, and −39%, respectively. Furthermore, the O3 concentration increments were 5%, 14%, 12%, and 14%. Second, we used a bottom-up approach to obtain the conclusion that the monthly averaged NO2 concentrations in Beijing, Shanghai, and Wuhan in March had the largest changes, changing to −39%, −40%, and −61%, respectively. The corresponding amounts of changes in monthly averaged O3 concentrations were −14%, −2%, and 9%. However, the largest amount of change in monthly averaged NO2 concentration in Guangzhou was found in December 2020, with a value of −40%. The change in O3 concentration was −12% in December. Finally, we analyzed the relationship of NO2 and O3 concentrations with XCO2. Moreover, the results show that the effect of NO2 concentration on XCO2 is positively correlated from the point of the satellite (R = 0.4912) and the point of the ground monitoring stations (R = 0.3928). Surprisingly, we found a positive (in satellite observations and R = 0.2391) and negative correlation (in ground monitoring stations and R = 0.3333) between XCO2 and the O3 concentrations. During the epidemic period, some scholars based on model analysis found that Wuhan’s carbon emissions decreased by 16.2% on average. Combined with satellite data, we estimate that Wuhan’s XCO2 fell by about 1.12 ppm in February. At last, the government should consider reducing XCO2 and NO2 concentration at the same time to make a synergistic reduction.

Dynamic Pluvial Flash Flooding Hazard Forecast Using Weather Radar Data

Pluvial flash floods are among the most dangerous weather-triggered disasters, usually affecting watersheds smaller than 100 km2, with a short time to peak discharge (from a few minutes to a few hours) after causative rainfall. Several warning systems in the world try to use this time lag to predict the location, extent, intensity, and time of flash flooding. They are based on numerical hydrological models processing data collected by on-ground monitoring networks, weather radars, and precipitation nowcasting. However, there may be areas covered by weather radar data, in which the network of ground-based precipitation stations is not sufficiently developed or does not even exist (e.g., in an area covered by portable weather radar). We developed a method usable for designing an early warning system based on a different philosophy for such a situation. This method uses weather radar data as a 2D signal carrying information on the current precipitation distribution over the monitored area, and data on the watershed and drainage network in the area. The method transforms (concentrates) the 2D signal on precipitation distribution into a 1D signal carrying information on potential runoff distribution along the drainage network. For sections of watercourses where a significant increase in potential runoff can be expected (i.e., a significant increase of the 1D signal strength is detected), a warning against imminent flash floods can be possibly issued. The whole curve of the potential runoff development is not essential for issuing the alarm, but only the significant leading edge of the 1D signal is important. The advantage of this procedure is that results are obtained quickly and independent of any on-ground monitoring system; the disadvantage is that it does not provide the exact time of the onset of a flash flooding or its extent and intensity. The generated alert only warns that there is a higher flash flooding hazard in a specific section of the watercourse in the coming hours. The forecast is presented as a dynamic map of the flash flooding hazard distribution along the segments of watercourses. Relaying this hazard to segments of watercourses permits a substantial reduction in false alarms issued to not-endangered municipalities, which lie in safe areas far away from the watercourses. The method was tested at the local level (pluvial flash floods in two small regions of the Czech Republic) and the national level for rainfall episodes covering large areas in the Czech Republic. The conclusion was that the method is applicable at both levels. The results were compared mainly with data related to the Fire and Rescue Service interventions during floods. Finally, the increase in the reliability of hazard prediction using the information on soil saturation is demonstrated. The method is applicable in any region covered by a weather radar (e.g., a portable one), even if there are undeveloped networks of rain and hydrometric gauge stations. Further improvement could be achieved by processing more extended time series and using computational intelligence methods for classifying the degree of flash flooding hazard on individual sections of the watercourse network.

Patterns of relationship between PM10 from air monitoring quality station and AOT data from MODIS sensor onboard of Terra satellite

Thailand, especially in the northern region, often encounters the problem of having PM10 exceeding the normal standard level, which could do harm to people’s health. Mostly, such problem is caused by the burning of forest area and open area; this is clearly seen during January–April of every year. Also, the problem as mentioned is caused by the meteorological conditions and the terrains in the northern region that make it easy for PM10 to be accumulated. The aim of this study was to analyze the patterns of relationship between PM10 measured from the ground monitoring station and AOT data received from MODIS sensor onboard of Terra satellite in Phrae Province located in the northern region of Thailand. The method performed was by analyzing the correlation between PM10 data obtained from the ground monitoring station and the AOT data received from the MODIS sensor onboard of Terra satellite during January–April 2018. It was found from the study that the change of the intensity of PM10 and AOT in the climate was highly related; it appeared that the correlation coefficient (r) in January–April was 0.92, 0.91, 0.91 and 0.92, respectively. This research pointed out that during February– –April, the areas of Phrae Province had the level of PM10 that affected health. Besides, from the method in this research, it revealed AOT data received from MODIS sensor onboard of Terra satellite could be applied in order to follow up, monitor, and notify the spatial changes of PM10 efficiently.

Colony Suppression and Possible Colony Elimination of the Subterranean Termites Coptotermes formosanus and Reticulitermes speratus by Discontinuous Soil Treatment Using a Diluent of Fipronil Suspension Concentrate

We assessed the efficacy of a discontinuous soil treatment using a diluent of fipronil suspension concentrate in controlling colonies of Coptotermes formosanus and Reticulitermes speratus. In-ground monitoring stations were installed at Isogi Park and Kindai University, and individual termites inhabiting the stations were collected for four or six years to determine the numbers and locations of colonies present in test areas before and after the discontinuous soil treatment. Microsatellite genotyping indicated that two C. formosanus and two R. speratus colonies in the test area at Isogi Park and five R. speratus colonies in the test area at Kindai University were active and that their territories fluctuated every year. One of the two C. formosanus colonies at Isogi Park and one of the five R. speratus colonies at Kindai University were subjected to discontinuous soil treatments with fipronil and were strongly affected by the treatment at the colony level, resulting in the suppression and possible elimination of colonies. Termite activity of the fipronil-treated colony of C. formosanus was detected within one week after the discontinuous soil treatment and was not found for more than two years (28 months), while termite activity of the fipronil-treated colony of R. speratus was detected within four days and three weeks after the discontinuous soil treatment and was not detected thereafter for three years. Fipronil residue analysis showed that workers of C. formosanus moved at least 28 m and that workers of R. speratus moved 6 m from the treated soil locations for up to three weeks.