Effectiveness of surface monitoring stations in representing regional CO<sub>2 emissions over India

Road surfaces should be maintained in excellent condition to ensure the safety of motorists. To this end, there exist various road-surface monitoring systems, each of which is known to have specific advantages and disadvantages. In this study, a smartphone-based dual-acquisition method system capable of acquiring images of road-surface anomalies and measuring the acceleration of the vehicle upon their detection was developed to explore the complementarity benefits of the two different methods. A road test was conducted in which 1896 road-surface images and corresponding three-axis acceleration data were acquired. All images were classified based on the presence and type of anomalies, and histograms of the maximum variations in the acceleration in the gravitational direction were comparatively analyzed. When the types of anomalies were not considered, it was difficult to identify their effects using the histograms. The differences among histograms became evident upon consideration of whether the vehicle wheels passed over the anomalies, and when excluding longitudinal anomalies that caused minor changes in acceleration. Although the image-based monitoring system used in this research provided poor performance on its own, the severity of road-surface anomalies was accurately inferred using the specific range of the maximum variation of acceleration in the gravitational direction.

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PM2.5 Concentration Forecasting at Surface Monitoring Sites Using GRU Neural Network Based on Empirical Mode Decomposition

The Science of The Total Environment ◽

10.1016/j.scitotenv.2020.144516 ◽

2021 ◽

pp. 144516

Author(s):

Guoyan Huang ◽

Xinyi Li ◽

Bing Zhang ◽

Jiadong Ren

Keyword(s):

Neural Network ◽

Empirical Mode Decomposition ◽

Mode Decomposition ◽

Surface Monitoring ◽

Pm2.5 Concentration

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Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-021-02071-4 ◽

2021 ◽

Author(s):

Christian Acal ◽

Ana M. Aguilera ◽

Annalina Sarra ◽

Adelia Evangelista ◽

Tonio Di Battista ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Functional Data ◽

Central Italy ◽

Principal Component ◽

Functional Principal Component Analysis ◽

Area Of Interest ◽

Pm 10 ◽

The Impact ◽

Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

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Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽

10.3390/s21103338 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3338

Author(s):

Ivan Vajs ◽

Dejan Drajic ◽

Nenad Gligoric ◽

Ilija Radovanovic ◽

Ivan Popovic

Keyword(s):

Machine Learning ◽

Air Quality ◽

Relative Humidity ◽

Low Cost ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Lower Accuracy ◽

Wide Range ◽

The Impact ◽

Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

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