Implementation of Integration VaaMSN and SEMAR for Wide Coverage Air Quality Monitoring

The current air quality monitoring system cannot cover a large area, not real-time and has not implemented big data analysis technology with high accuracy. The purpose of an integration Mobile Sensor Network and Internet of Things system is to build air quality monitoring system that able to monitor in wide coverage. This system consists of Vehicle as a Mobile Sensors Network (VaaMSN) as edge computing and Smart Environment Monitoring and Analytic in Real-time (SEMAR) cloud computing. VaaMSN is a package of air quality sensor, GPS, 4G WiFi modem and single board computing. SEMAR cloud computing has a time-series database for real-time visualization, Big Data environment and analytics use the Support Vector Machines (SVM) and Decision Tree (DT) algorithm. The output from the system are maps, table, and graph visualization. The evaluation obtained from the experimental results shows that the accuracy of both algorithms reaches more than 90%. However, Mean Square Error (MSE) value of SVM algorithm about 0.03076293, but DT algorithm has 10x smaller MSE value than SVM algorithm

Multi-horizon air pollution forecasting with deep neural networks

Air pollution is a global problem, especially in urban areas where the population density is very high due to the diverse pollutant sources such as vehicles, industrial plants, buildings, and waste. North Macedonia, as a developing country, has a serious problem with air pollution. The problem is highly present in its capital city, Skopje, where air pollution places it consistently within the top 10 cities in the world during the winter months. In this work, we propose using Recurrent Neural Network (RNN) models with long short-term memory units to predict the level of PM10 particles at 6, 12, and 24 h in the future. We employ historical air quality measurement data from sensors placed at multiple locations in Skopje and meteorological conditions such as temperature and humidity. We compare different deep learning models’ performance to an Auto-regressive Integrated Moving Average (ARIMA) model. The obtained results show that the proposed models consistently outperform the baseline model and can be successfully employed for air pollution prediction. Ultimately, we demonstrate that these models can help decision-makers and local authorities better manage the air pollution consequences by taking proactive measures

Air pollution prediction with multi-modal data and deep neural networks

Air pollution is becoming a rising and serious environmental problem, especially in urban areas affected by an increasing migration rate. The large availability of sensor data enables the adoption of analytical tools to provide decision support capabilities. Employing sensors facilitates air pollution monitoring, but the lack of predictive capability limits such systems’ potential in practical scenarios. On the other hand, forecasting methods offer the opportunity to predict the future pollution in specific areas, potentially suggesting useful preventive measures. To date, many works tackled the problem of air pollution forecasting, most of which are based on sequence models. These models are trained with raw pollution data and are subsequently utilized to make predictions. This paper proposes a novel approach evaluating four different architectures that utilize camera images to estimate the air pollution in those areas. These images are further enhanced with weather data to boost the classification accuracy. The proposed approach exploits generative adversarial networks combined with data augmentation techniques to mitigate the class imbalance problem. The experiments show that the proposed method achieves robust accuracy of up to 0.88, which is comparable to sequence models and conventional models that utilize air pollution data. This is a remarkable result considering that the historic air pollution data is directly related to the output—future air pollution data, whereas the proposed architecture uses camera images to recognize the air pollution—which is an inherently much more difficult problem

Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model's performance, especially for t + 4 (rho approximate to 0.68 to rho approximate to 0.74) and t + 8 (rho approximate to 0.59 to rho approximate to 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases

A forecast of surface ozone using analytical models

In this study, several analytical models were tested to forecast the surface ozone concentration using Artificial Neural Network (ANN), Multiple Linear Regression (MLR) and Time Series Regression (TSR). Four study areas were selected in this study, namely Seberang Jaya in Penang, Shah Alam in Selangor, Larkin in Johor and Kota Bharu in Kelantan. The main objective of this study is to determine the appropriate analytical models MLR and ANN for surface ozone forecasting in some zones of peninsular Malaysia, to forecast surface ozone concentration with TSR model in several zones of peninsular Malaysia and to compare the performance of each model by the performance index. The performance index that will be shown in this study for the model comparison are root mean square error (RMSE), mean square error (MSE) and determination of coefficient (R2). The ANN model showed better performance compared to the MLR and TSR models in the model comparison in each station. The station in Larkin, Johor provides high accuracy in forecasting surface ozone concentrations for each model with minimum MSE, 0.000009 ppm and RMSE, 0.0042 ppm compared to other stations. The value of R2 is 0.33 which is highest compared to station in Seberang Jaya and Kota Bharu

Imputation through Clustering of Time Series Data: a case study in air pollution

Air pollution is a global problem, and air pollution concentration assessment plays an essential role in evaluating the associated risk to human health. Unfortunately, air pollution monitoring stations often have periods of missing data. In this thesis, we investigated missing values problem in air quality data by looking at the hourly pollutant concentration Time Series (TS) of the main four pollutants included in air quality assessment: O3, NO2, PM2.5, and PM10. The research presented in this thesis aims to reduce the uncertainty of the air quality assessment by proposing methods for the imputation of missing values either partially or completely. Our approach uses clustering of stations based on measured pollutants to inform the imputation. We started by testing uni-variate clustering and then developing a multivariate time series (MVTS) clustering method that considers all measured pollutants at a station by aggregating the similarity between those pollutants (through a fused distance) followed by imputation models for the whole TS. We developed various imputation models including ensemble models which aggregate temporal similarity obtained from clustering and spatial similarity obtained by the geographical correlation between stations. Our experimental results show that using MVTS clustering enables imputation of unmeasured pollutants in any station and produced plausible imputed values for all pollutants. Ensemble imputation models (Model 8 and 9) gave the lowest RMSE, the highest (IOA) between imputed and real values, and met the minimum requirement criteria using FAC2 for air quality modelling. The imputation models reproduce high pollution episodes at stations within the clusters where these episodes possibly happened but were not measured, as some of them were captured by the cluster centroids. We also found two important pollutants associated with those episodes: PM2.5 and O3 which may require more measures or should be imputed in different locations for more realistic air quality monitoring