Sensors and systems for air quality assessment monitoring and management: A review.

Air quality (AQ) is a global concern for human health management. Therefore, air quality monitoring (AQM) and its management is a must-needed activity for the current world environment. A systematic review of various sensors and systems for AQ management may strengthen our understanding of the monitoring and management of AQ. Thus, the current review presents details on sensors/systems available for AQ assessment, monitoring, and management. First, we had gone through the published literature based on special keywords including AQM, Particulate Matter (PM), Carbon Mono-oxide (CO), Sulfur di-Oxide (SO2), and Nitrogen di-Oxide (NO2) among others, and identified the current scenario of research in AQ management. We discussed various sensors/systems available for the AQ management based on self-conceptualised five major categories including, ground-based AQS (wet chemistry) systems, ground-based digital sensors systems, aerial sensors systems, satellite-based sensors systems, and integrated systems. The prospects in the field of AQ assessment and management (AQA&M) were then discussed in detail. We concluded that the AQA&M can be better achieved by coupling new technologies like ground-based smart sensors, satellite remote sensing sensors, Geospatial technologies, and computational technologies like machine learning, Artificial intelligence, and Internet of Things (IoT). The current work may lead to a junction of information for connecting these sensors/systems, which is expected to be beneficial in future AQ research and management.

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

BACKGROUND: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. OBJECTIVE: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. METHODS: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. RESULTS: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. CONCLUSIONS: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.