scholarly journals Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4717
Author(s):  
Yacine Mohamed Idir ◽  
Olivier Orfila ◽  
Vincent Judalet ◽  
Benoit Sagot ◽  
Patrice Chatellier
Keyword(s):  
Air Quality ◽  
Mean Squared Error ◽  
Mobile Network ◽  
Urban Environments ◽  
Environmental Sensors ◽  
Simple Kriging ◽  
Distance Weighting ◽  
Geostatistical Models ◽  
Spatio Temporal ◽  
Inverse Distance

With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.

scholarly journals Assessing the Immediate Effect of Covid-19 Lockdown on Air Quality: A Case Study of Delhi, India

2020 ◽  
Vol 13 (3-4) ◽  
pp. 27-33
Author(s):  
Ankit Sikarwar ◽  
Ritu Rani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Interpolation ◽  
World Health ◽  
Guideline Values ◽  
Distance Weighting ◽  
Health Organization ◽  
The City ◽  
Inverse Distance

Abstract In India, a nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants: particulate matter sized 2.5 μm (PM2.5) and 10 μm (PM10), and nitrogen dioxide (NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate decreasing levels of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that one month before the lockdown the levels of air pollution in Delhi were critical and much higher than the guideline values set by the World Health Organization. The levels of air pollution became historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

APPLICATION OF AIR QUALITY INDEX AND INVERSE DISTANCE WEIGHTING FOR MAPPING THE DISTRIBUTION OF AIR POLLUTION AT SEVERAL URBAN DISTRICTS OF HANOI CITY

2020 ◽  
Vol 65 (10) ◽  
pp. 189-200
Author(s):  
Khac Dang Vu ◽  
Anh Nguyen Thi Van
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Inverse Distance Weighting ◽  
Air Quality Index ◽  
Pollution Level ◽  
Urban Districts ◽  
Distance Weighting ◽  
Inverse Distance

The air pollution level can be assessed using air quality index - AQI calculated from the concentration of some gases and particle matters which are measured at ambient air quality monitoring stations. The calculated AQI values are characterized by temporal continuity but spatial discontinuity. However, AQI values of each monitoring station is interpolated by the IDW (Inverse Distance Weighting) method in GIS which helps us to assess the air quality at a detailed and specific level for every location in the study area by establishing distribution maps of air pollution. The interpolation of AQI values for zoning air quality in several urban districts of Hanoi during the Winter (October, November, December 2019) shows that in general, the areas with a very bad level of air quality occupied an important surface in the Northwest of urban districts (on the territory of Bac Tu Liem, Ba Dinh, Tay Ho, Cau Giay) for last 3 months of the year. The areas with a bad level of air quality occupied a large surface in the Southeast in October and December, but its surface became narrow in November. But in November, areas having a bad level of air quality were expanded to the Southeast while they occupied only a small surface at the center of the study area in October and December. Although the distribution of each level vary in terms of coverage, their common pattern has been conserved during three months of Winter. The distribution map of air quality provides the complete picture of the air pollution situation and it helps to adequately evaluate this issue in the urban districts of Hanoi city.

scholarly journals Assessing the immediate effect of COVID-19 lockdown on air quality: A case study of Delhi, India

2020 ◽  
Author(s):  
Ankit Sikarwar ◽  
Ritu Rani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Spatial Interpolation ◽  
World Health ◽  
Guideline Values ◽  
Distance Weighting ◽  
Health Organization ◽  
The City ◽  
Inverse Distance

Abstract In India, the nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants (PM2.5, PM10, and NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate the lowering of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that before one month of the lockdown the levels of air pollution in Delhi were critically high and far beyond the guideline values set by the World Health Organization. The levels of air pollution are historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

scholarly journals The Effect of the COVID-19 Pandemic on Ambient Air Quality in Yogyakarta Urban Area Parameters SO2, CO and, NO2 with Inverse Distance Weighting (IDW)

2021 ◽  
Vol 933 (1) ◽  
pp. 012013
Author(s):  
E R Sihayuardhi ◽  
W Brontowiyono ◽  
F B Maziya ◽  
L Hakim
Keyword(s):  
Air Quality ◽  
Urban Area ◽  
Urban Areas ◽  
Ambient Air ◽  
Major Road ◽  
Ambient Air Quality ◽  
Distance Weighting ◽  
Tourist Center ◽  
Inverse Distance ◽  
Special Region

Abstract In early March 2020, a pandemic disrupted human activities, especially in the Yogyakarta Urban Area. The area is experiencing development or becoming a tourist center in the Province of the Special Region of Yogyakarta. With these developments, this study would carry out data processing related to air quality, remove it comparing the concentration of SO2, CO, and NO2 parameters before the pandemic in 2019 and after the pandemic in 2020. The results of this study are that the ambient air quality in urban areas Yogyakarta tends to experience a decline and the monitoring locations are located around industries and major road junctions. So, the mapping of ambient air quality itself can facilitate visualization research.

scholarly journals Spatio-temporal analysis of air quality and its relationship with major COVID-19 hotspot places in India

2021 ◽  
Vol 22 ◽  
pp. 100473
Author(s):  
Hasan Raja Naqvi ◽  
Guneet Mutreja ◽  
Adnan Shakeel ◽  
Masood Ahsan Siddiqui
Keyword(s):  
Air Quality ◽  
Temporal Analysis ◽  
Spatio Temporal

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

scholarly journals Spatial Distribution of Soil Nutrients in Farmland in a Hilly Region of the Pearl River Delta in China Based on Geostatistics and the Inverse Distance Weighting Method

Agriculture ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Rumi Wang ◽  
Runyan Zou ◽  
Jianmei Liu ◽  
Luo Liu ◽  
Yueming Hu
Keyword(s):  
Spatial Distribution ◽  
Pearl River Delta ◽  
Pearl River ◽  
Weighting Method ◽  
Farmland Protection ◽  
The Pearl River Delta ◽  
Hilly Region ◽  
Distance Weighting ◽  
The Pearl River ◽  
Inverse Distance

Soil nutrients are essential factors that reflect farmland quality. Nitrogen, phosphorus, and potassium are essential elements for plants, while silicon is considered a “quasi-essential” element. This study investigated the spatial distribution of plant nutrients in soil in a hilly region of the Pearl River Delta in China. A total of 201 soil samples were collected from farmland topsoil (0–20 cm) for the analysis of total nitrogen (TN), available phosphorus (AP), available potassium (AK), and available silicon (ASi). The coefficients of variation ranged from 47.88% to 76.91%. The NSRs of TN, AP, AK, and ASi were 0.15, 0. 07, 0.12, and 0.13, respectively. The NSRs varied from 0.02 to 0.20. All variables exhibited weak spatial dependence (R2 < 0.5), except for TN (R2 = 0.701). After comparing the prediction accuracy of the different methods, we used the inverse distance weighting method to analyze the spatial distribution of plant nutrients in soil. The uniform spatial distribution of AK, TN overall showed a trend of increasing from northeast to southwest, and the overall spatial distribution of AP and ASi showed that the northeast was higher than the southwest. This study provides support for the delimitation of basic farmland protection areas, the formulation of land use spatial planning, and the formulation of accurate farmland protection policies.

scholarly journals Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1535
Author(s):  
Tonggang Fu ◽  
Hui Gao ◽  
Jintong Liu
Keyword(s):  
Soil Salinity ◽  
Subsurface Layer ◽  
Salt Content ◽  
Spatial Prediction ◽  
Northern China ◽  
Sodium Adsorption Ratio ◽  
Interpolation Methods ◽  
Distance Weighting ◽  
Total Salt Content ◽  
Inverse Distance

Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.

Sign in / Sign up

Export Citation Format

Share Document