Hyperspectral sensing of heavy metals in soil by integrating AI and UAV technology.

Given the limitation of conventional soil pollution monitoring through mapping which is a costly, time-consuming work, the study aims to establish an image recognition model to identify the source of pollution automatically. The study choses a contaminated land and then use a non-destructive instrument that can quickly and effectively measure the content of heavy metals. A two concentration prediction models of Ni, Cu, Zn, Cr, Pb, As, Cd, and Hg using hyperspectral imaging were developed, Decision Tree and Back Propagation Neural Network, in combination of particle swarm optimization employed for optimization algorithm. As a result, random forest is more accurate than the forecast result of back propagation neural network. This study has established an excellent Cu and Cr model, which can accurately capture the pollution source. In addition, through aerial photographs, we also found that there were also high pollution reactions on the banks of the river. The developed model is beneficial for high pollution areas which can be quickly found, thereby following investigation and remediation work can be carried out with less time and cost consuming comparing with the conventional soil monitoring.

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA.

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control.

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks' precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

Impact assessment of river dust on regional air quality through integrated remote sensing and air quality modeling.

Sand and dust storms in arid and semiarid regions deteriorate regional air quality and threaten public health security. To quantify the negative effects of river dust on regional air quality, this study selected the estuary areas located in central Taiwan as a case study and proposed an integrated framework to measure the fugitive emission of dust from riverbeds with the aid of satellite remote sensing and wind tunnel test, together with the concentrations of particulate matter with a diameter of <10 µm (PM10) around the river system by using The Air Pollution Model. Additionally, the effects of 25 types of meteorological conditions on the health risk due to exposure to dust were evaluated near the estuary areas. The results reveal landscape changes in the downstream areas of Da'an and Dajia rivers, with an increase of 370,820 m2 and 1,554,850 m2 of bare land areas in the dry season compared with the wet season in Da'an and Dajia rivers, respectively. On the basis of the maximum emission of river dust, PM10 concentration increases considerably during both wet and dry seasons near the two rivers. Among 25 different types of weather conditions, frontal surface transit, outer-region circulation from tropical depression system, weak northeast monsoons, and anticyclonic outflow have considerable influence on PM10 diffusion. In particular, weak northeast monsoons cause the highest health risk in the areas between Da'an and Dajia rivers, which is the densely populated Taichung City. Future studies should attempt to elucidate the environmental impact of dust in different weather conditions and understand the spatial risks to human health due to PM10 concentration. Facing the increasing threat of climate and landscape changes, governments are strongly encouraged to begin multimedia assessments in environmental management and propose a long-term and systematic framework in resources planning.