Validation and Spatiotemporal Analysis of the Modis 1 Km Aerosol Product over China during the Covid-19 Pandemic

The Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km aerosol product based on the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has great potential in understanding the interaction between human activities and the atmospheric environment. In this paper, the MODIS 1 km aerosol product over China during the Coronavirus Disease 2019 (COVID-19) pandemic was validated against with the ground measured data collected from the Aerosol Robotic Network (AERONET). The result shows a good agreement between the two datasets. The spatiotemporal analyses of three selected regions, which are Beijing-Tianjin-Hebei, Hubei and Guangdong-Hong Kong-Macao, indicate that the COVID-19 pandemic has a significant impact on human activities and aerosol loadings.

Comparison of Atmospheric Environment in China during Sars and Covid-19 Pandemics

Satellite remote sensing has advantages in monitoring environmental changes during the global pandemics such as the Severe Acute Respiratory Syndrome Coronavirus (SARS) and the Corona Virus Disease 2019 (COVID-19). In this paper, the variations of atmospheric environment during SARS and COVID-19 pandemics were calculated and analyzed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Atmosphere Monthly Global Product. Preliminary results show that: (1) aerosol optical depth is most affected by the pandemics, especially the duration and prevention and control measures;(2) the correlations between the variables of aerosol optical depth, cloud fraction, total column ozone and precipitable water vapor were not very strong during the two pandemics.

Updating and Evaluating Anthropogenic Emissions for NOAA's Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of an SO2 Bias-Scaling Method

We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.

Effect of Transportation Operation on Air Quality in China Based on MODIS AOD during the Epidemic

With the rapid growth of automobile numbers and the increased traffic congestion, traffic has increasingly significant effects on regional air quality and regional sustainable development in China. This study tried to quantify the effect of transportation operation on regional air quality based on MODIS AOD. This paper analyzed the space-time characteristics of air quality and traffic during the epidemic by series analysis and kernel density analysis, and quantified the relationship between air quality and traffic through a Geographically Weighted Regression (GWR) model. The main research conclusions are as follows: The epidemic has a great impact on traffic and regional air quality. PM2.5 and NO2 had the same trend with traffic congestion delay index (CDI), but they were not as obvious as CDI. Both cities with traffic congestion and cities with the worst air quality showed strong spatial dependence. The concentration areas of high AOD value in the east areas of the Hu line were consistent with the two gathering centers formed by cities with traffic congestion in space, and also consistent with the gathering center of cities with poor air quality. The concentration area of AOD decline was consistent with the gathering center formed by cities with the worst air quality. AOD had a strong positive correlation with road network density, and its GWR correlation coefficient was 0.68, then These provinces suitable for GWR or not suitable were divided. This study has a great significance for the transportation planning, regional planning, air quality control strategies and regional sustainable development, etc.

Identifying Basal Nighttime Radiance Levels for Estimating Traffic Flow based on VIIRS/DNB data

The recent COVID-19 outbreak drove the attention to methods for monitoring the flow of people between human settlements, including traffic flow. Although the remote sensing of nighttime lights is a viable option to estimate traffic flow-derived indicators, changes in radiance levels at night are not all associated with traffic. This paper presents the theoretical approach proposed on the development of an algorithm able to identify spectrally unbiased control samples for regions of interest (ROI), namely roadway sections. Firstly, an experiment is presented to put in evidence the background dependency of the DNB monthly composites (vcm) radiance levels. Then, an overview of the algorithm is presented, followed by an empirical estimation of its time complexity. The results showed that the algorithm has an O(n) time complexity and that control samples and ROIs can have similar time series features, indicating that analysis without the use of control samples can lead to biased results. © 2021 Society for Industrial and Applied Mathematics.

Air Quality Improvement Following COVID-19 Lockdown Measures and Projected Benefits for Environmental Health

Many regions worldwide suffer from heavy air pollution caused by particulate matter (PM2.5) and nitrogen dioxide (NO2), resulting in a huge annual disease burden and significant welfare costs. Following the outbreak of the COVID-19 global pandemic, enforced curfews and restrictions on human mobility (so-called periods of 'lockdown') have become important measures to control the spread of the virus. This study aims to investigate the improvement in air quality following COVID-19 lockdown measures and the projected benefits for environmental health. China was chosen as a case study. The work projects annual premature deaths and welfare costs by integrating PM2.5 and NO2 pollutant measurements derived from satellite imagery (MODIS instruments on Terra and Aqua, and TROPOMI on Sentinel-5P) with census data archived by the Organization for Economic Co-operation and Development (OECD). A 91-day timeframe centred on the initial lockdown date of 23 January 2020 was investigated. To perform the projections, OECD data on five variables from 1990 to 2019 (mean population exposure to ambient PM2.5, premature deaths, welfare costs, gross domestic product and population) were used as training data to run the Autoregressive Integrated Moving Average (ARIMA) and multiple regression models. The analysis of the satellite imagery revealed that across the regions of Beijing, Hebei, Shandong, Henan, Xi'an, Shanghai and Hubei, the average concentrations of PM2.5 decreased by 6.2, 30.7, 14.1, 20.7, 29.3, 5.5 and 17.3%, while the NO2 decreased by 45.5, 54.7, 60.5, 58.7, 63.6, 50.5 and 66.5%, respectively, during the period of lockdown restrictions in 2020, as compared with the equivalent period in 2019. Such improvements in air quality were found to be beneficial, reducing in 2020 both the number of premature deaths by approximately 97,390 and welfare costs by over USD 74 billion.

Spatio-temporal analysis of air quality and its relationship with COVID-19 lockdown over Dublin.

Air pollution has become one of the biggest challenges for human and environmental health. Major pollutants such as Nitrogen Dioxide (NO 2 ), Sulphur Dioxide (SO 2 ), Ozone (O 3 ), Carbon Monoxide (CO), and Particulate matter (PM10 and PM2.5) are being ejected in a large quantity every day. Initially, authorities did not implement the strictest mitigation policies due to pressures of balancing the economic needs of people and public safety. Still, after realizing the effect of the COVID-19 pandemic, countries around the world imposed a complete lockdown to contain the outbreak, which had the unexpected benefit of causing a drastic improvement in air quality. The present study investigates the air pollution scenarios over the Dublin city through satellites (Sentinel-5P and Moderate Resolution Imaging Spectroradiometer) and ground-based observations. An average of 28% reduction in average NO 2 level and a 27.7% improvement in AQI (Air Quality Index) was experienced in 2020 compared to 2019 during the lockdown period (27 March-05 June). We found that PM10 and PM2.5 are the most dominating factor in the AQI over Dublin.

Spatial distribution of aerosol optical depth over India during COVID-19 lockdown phase-1

The present study analysed the spatial distribution of Aerosol Optical Depth (AOD) over India during the COVID-19 lockdown phase -1 (March 25 to April 15, 2020) using MODIS Terra (MOD04) AOD data (550 nm) during 2001-2020. Air temperature, rainfall, forest fire incidents, and wind patterns were analysed to understand their effect on the distribution of aerosols over India during the lockdown phase-1. Moderate absorption fine aerosol type is predominant but sparsely distributed over India during the study period compared to the reference period indicating the positive influence of the lockdown. Mean AOD has reduced by 9% over India during the lockdown phase-1 compared to the corresponding mean of the past 19 years (2001-2019). About 70% of the states/UTs of India showed a reduction in mean AOD due to restrictions on non-essential economic activities and rainfall occurrence. However, some states showed an increase in aerosol loading over specific pockets despite the restrictions on economic activities (Arunachal Pradesh, Assam, Gujarat, Orissa, Andhra Pradesh, Madhya Pradesh, Chhattisgarh, Maharashtra, Assam, Nagaland, Manipur and Karnataka) because of active forest fire cases. This study would be helpful for planners and policymakers to adopt suitable measures to control the rising concentrations of aerosols over hotspot regions of India.

Effect of Seasonal Land Surface Temperature Variation on COVID-19 Infection Rate: A Google Earth Engine-Based Remote Sensing Approach.

This study aims to identify the effect of seasonal land surface temperature variation on the COVID-19 infection rate. The study area of this research is Bangladesh and its 8 divisions. The Google Earth Engine (GEE) platform has been used to extract the land surface temperature (LST) values from MODIS satellite imagery from May 2020 to July 2021. The per-day new COVID-19 cases data has also been collected for the same date range. Descriptive and statistical results show that after experiencing a high LST season, the new COVID-19 cases rise. On the other hand, the COVID-19 infection rate decreases when the LST falls in the winter. Also, rapid ups and downs in LST cause a high number of new cases. Mobility, social interaction, and unexpected weather change may be the main factors behind this relationship between LST and COVID-19 infection rates.

IrriMap_CN: Annual irrigation maps across China in 2000–2019 based on satellite observations, environmental variables, and machine learning

Agricultural irrigation, as an important practice to protect crops from drought and promote grain yield, has a long history in China. A timely and precise dataset about the extent and dynamics of irrigated areas is necessary for water allocation and agricultural management but is scarce in China. Here we developed annual irrigated cropland maps across China (IrriMap_CN) at 500-m resolution from 2000 to 2019, using MODIS data, machine-learning method, and Google Earth Engine platform. First, we generated annual nationwide training samples by strictly screening the existing irrigation maps downscaled from the statistical data. Second, we implemented locally adaptive random forest classifiers in 511 nominal 1° × 1° grid cells across China with MODIS vegetation indices, climatic factors, and topography variables. Third, we conducted nationwide pixel-wise validation of the IrriMap_CN using independent samples. The validation results based on more than 3000 ground truth points revealed that IrriMap_CN had high accuracies ranging from 77.2% to 85.9%. The time series of IrriMap_CN detected substantial expansion of irrigated areas in Xinjiang and Heilongjiang (more than 50% in total) and pronounced decreases in Sichuan, Jiangsu, and Hebei. The analyses of irrigation frequency, start time, and end time implied that North China Plain was the most intensive irrigated area;but the irrigation area showed a decreasing trend since 2000, consistent with the reduced agricultural water consumption. The annual irrigation datasets allow us to understand the spatiotemporal dynamics of irrigated croplands in China and are expected to contribute to the improvement of earth system models and facilitate sustainable agricultural water management. • Annual irrigation maps (IrriMap_CN) were generated for China in 2000–2019. • Nationwide training samples were extracted from existing irrigation maps. • IrriMap_CN highlights the declining irrigation area in North China Plain. • Cropland reclamation/occupation and water supply are key to irrigation area changes. [ FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

• MOD16 products indicated significant underestimations in all paddy rice ET observations. • R n estimation in overcast conditions and LAI reconstruction were two key causes. • Daily R n estimations under all-sky conditions by a global cloudy index algorithm were improved by 40.6%. • Daily LAI dynamics estimated by the LTDG_PhenoS algorithm were improved by 818.7%. • Daily ET estimations were improved by 68.7%. Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (R n) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved R n estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily R n under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Changes in air quality over different land covers associated with COVID-19 in Turkey aided by GEE.

With the increased urbanization, the rise of the manufacturing industry, and the use of fossil fuels, poor air quality is one of the most serious and pressing problems worldwide. The COVID-19 outbreak prompted absolute lockdowns in the majority of countries throughout the world, posing new research questions. The study's goals were to analyze air and temperature parameters in Turkey across various land cover classes and to investigate the correlation between air and temperature. For that purpose, remote sensing data from MODIS and Sentinel-5P TROPOMI were used from 2019 to 2021 over Turkey. A large amount of data was processed and analyzed in Google Earth Engine (GEE). Results showed a significant decrease in NO2 in urban areas. The findings can be used in long-term strategies for lowering global air pollution. Future research should look at similar investigations in various study sites and evaluate changes in air metrics over additional classes.

Development of the TOA-Related Models for PM2.5 Prediction Pre- and Post-COVID-19 Outbreak over Yangtze River Delta Region of China

The lockdown and the strict regulation measures implemented by Chinese government due to the outbreak of the COVID-19 pandemic not only decelerated the spread of the virus but also brought a positive effect on the nationwide atmospheric quality. In this study, we extended our previous research on remotely sensed estimation of PM2.5 concentrations in Yangtze River Delta region (i.e., YRD) of China from 2019 to the strict regulation period of 2020 (i.e., 24 Jan, 2020-31 Aug, 2020). Unlike the method using aerosol optical depth (AOD) developed in previous studies, we validated the possibility of moderate resolution imaging spectroradiometer (MODIS) top-of-atmosphere (TOA) reflectance (i.e., MODIS TOA) at 21 bands in estimating the PM2.5 concentrations in YRD region. Two random forests (i.e., TOA-sig RF and TOA-all RF) incorporated with different MODIS TOA datasets were developed, and the results showed that the TOA-sig RF model performed better with R2 of 0.81 (RMSE=8.07 μg/m3) than TOA-all RF model with R2 of 0.79 (RMSE=9.13 μg/m3). The monthly averaged PM2.5 exhibited the highest value of 50.81 μg/m3 in YRD region in January 2020 and sharply decreased from February to August 2020. The annual mean PM2.5 concentrations derived by TOA-sig RF model were 47.74, 32.14, and 21.04 μg/m3 in winter, spring, and summer in YRD during the strict regulation period of 2020, respectively, showing much lower values than those in 2019. Our research demonstrated that the PM2.5 concentrations could be effectively estimated by using MODIS TOA reflectance at 21 bands and the random forest.

Variations of Aerosol Radiative Forcing During Covid-19 Imposed Lockdown Over Ahmedabad City

In the present study Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellite derived Aerosol Optical Depth (AOD) and the Ozone Monitoring Instrument (OMI) onboard Aura satellite derived Single Scattering Albedo (SSA) data sets were used to demonstrate the regional variation in aerosol radiative forcing during covid-19 imposed lockdown over the urban climate of Ahmedabad city. An analysis of short-wave (0.25um to 4.0 um) Instantaneous Direct Aerosol Radiative forcing (IDARF) is done using these satellite data as inputs to the Radiative Transfer model - SBDART. Result shows reduction in IDARF by the month of April-2020 and highest reduction in the month of May. Value of IDARF for May is around 22.785 Wm-2, which is 40.21% less than the mean value of IDARF from pre lockdown to post lockdown. Which indicates Negative Radiative Forcing (Net Cooling Effect). Magnitude of IDARF during lockdown and post lockdown are found to be 34.49 Wm-2 and 71.62 Wm-2 which is 87.94% higher than the mean value of IDARF from pre lockdown to post lockdown. Which suggest Positive Radiative Forcing (Net Warming Effect). © 2021 IEEE.

Investigating The Effect of Covid Induced Lockdown On Land Surface Temperature Over Ahmedabad City

The present study focuses over Ahmedabad City of Gujarat State, India for the time period 1st March to 30th June comprising of the Pre-Lockdown Phase (PLP), the National Lockdown Phase - 1 (NLP1) and the Unlock Phase - 1 (ULP1). We have considered this time period over the years 2019, 2020 and 2021 to explore the effect of COVID induced lockdown on LST and understanding its variation. Satellite data acquired from AQUA - MODIS with a spatial and temporal resolution of 1 Km and 1-2 days respectively was used for the analysis of the LST. The average LST over Ahmedabad was 314.18 K, 311.79 K and 315.67 K for PLP over the years 2019, 2020 and 2021. For NLP1 the average LST over those years were 321.68 K, 318.73 K and 319.39 K respectively. And for the ULP1 the average LST over those years were 319.87 K, 314.07 K and 312.19 K respectively. We observe a 2.38 %, 2.22 % and 1.17 % increase in LST from the PLP to NLP1 during the years 2019, 2020 and 2021. The increase of LST during the NLP1 in 2020 showed that as the pollution decreased, the active elements that were present in the atmosphere which caused disturbance to the sensor on the satellite while calculating LST were reduced and we got a brighter top of surface. The decrease in LST from 2019 levels for the ULP1 is also observed indicating the effects of lockdown and onset of monsoon in 2020 and 2021. © 2021 IEEE.

Effects of COVID-19 pandemic lockdown: A satellite data-based appraisal of air quality in Guwahati, Assam.

Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) based data are used to evaluate the effects of the COVID-19 lockdown on the concentrations of pollutants such as aerosol optical depth (AOD) and tropospheric columns of nitrogen dioxide (NO2) along with sulfur dioxide (SO2) respectively for the period of January 2017 to September 2021 over the capital city of Assam, Guwahati. In India lockdown due to COVID-19 was first imposed from 24th March to 14th April as phase I and then it extended from 15th April to 3rd May as phase II in the year 2020. The concentration of all pollutants was usually fall during the lockdown period as compared to their average during the 5-year period over the study area. The results showed that Pre-monsoon (March-May) seasonal AOD for the pandemic year 2020 was decreased by âˆ¼ 23% after lockdown as compared to same season of normal years over the study location. The seasonally averaged AOD reached its peak value in pre-monsoon (0.78 ± 0.09), followed by winter (0.59 ± 0.10) and monsoon (0.52 ± 0.05), with the minimum taking place in post-monsoon (0.38 ± 0.08) season. The monthly average AOD varies from its highest value (0.82 ± 0.18) in May to its lowest value (0.36 ± 0.10) in October for the study period over Guwahati. Tropospheric column NO2 exhibits same seasonality as AOD with highest value (0.21 × 1016 molecules cm-2) in pre-monsoon and lowest value (0.13 × 1016 molecules cm-2) in post-monsoon season which may be due to same source of origination of both NO2 and AOD. Conversely, SO2 does not vary much from the five-year average value during the lockdown period. Significant reduction in PM2.5 mass concentration value during Covid-19 lockdown months has been observed which indicates short term improvement of air quality over Guwahati.

Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area. [ FROM AUTHOR] Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

VARIATION OF SATELLITE-DERIVED AEROSOL OPTICAL DEPTH OVER CHINA BEFORE AND AFTER THE COVID-19 PANDEMIC

Due to the Coronavirus Disease (COVID-19) pandemic, the human activities in China and even in the world were reduced in 2020, which also caused the variation of the atmospheric environment, especially atmospheric aerosol emissions. In this paper, the MODIS level-3 gridded atmosphere monthly global joint product in 2019 and 2020 were collected and processed. After preliminary analysis, we found that MODIS annual aerosol optical depth (AOD) over China in 2020 is generally lower than in 2019. In some regions such as Beijing-Tianjin-Hebei and Yangtze River Delta, AOD values dropped the most in February. However, in some months and regions, AOD in 2020 is even higher than in 2019. More studies are still ongoing. © 2021 IEEE.

Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from high-resolution satellite aerosol optical depth

The mathematical solution to estimate surface fine particulate matter (PM2.5) from columnar aerosol optical depth (AOD) includes complex variables and involves a bunch of assumptions. Hence, researchers tend to use training-based models to predict PM2.5 from AOD. Here, we integrated regulatory composite PM2.5 measurements, high-resolution satellite AOD, reanalysis meteorological parameters, and a few other auxiliary parameters to train ten different regression models. The performance of these (seven statistical and three machine learning) models was evaluated and inter-compared to identify the best performing model. The accuracies of the model predicted PM2.5 were quantified based on the coefficient of determination (R2), mean absolute bias (MAB), normalized root mean square error (NRMSE), and other relevant regression coefficients. The model's performance on unseen data was investigated in terms of 10-fold cross-validation (CV) and Leave-one station-out CV (LOOCV). For this exercise, we considered the case of NCT-Delhi due to: (i) the availability of dense regulatory PM2.5 measurements, (ii) the possibility of understanding the model performance over a large range of PM2.5 (the daily mean PM2.5 values ranged between ∼ 4 and 492 μg m−3 during the study period), and (iii) the scope of better understanding the influence of extreme meteorological conditions (e.g. the ambient surface temperature varies between ∼5 and 40 °C during a calendar year) on the AOD-PM2.5 relationship. All the models were trained using data collected for the year 2019 (a non-COVID year). Among models under investigation, Machine Learning (ML) models performed better with R2, MAB, and NRMSE values for the CV exercises ranging between 0.88 and 0.93, 14.1 and 18.2 μg m−3, and 0.18 and 0.23, respectively. The generalizability of the results obtained in this study was discussed.

Use of MODiS-MAiAC (aod) Satellite Images as Qualitative Indicators of the Concentration of Particulate Matter pm2.5 in Bogota City

Particulate matter is the most-related contaminant to respiratory and cardiac diseases in the planet. In Colombia, it is frequently monitored as concentration of PM25 with air quality stations, that are run by government organizations. In addition to monitoring in some countries, the use of satellite images with AOD (Aerosol Optical Depth) has recently become popular to estimate PM2y however, in Colombia, this alternative has not been explored yet. This research seeks to assess the potential use of MODIS-MAIAC images as a qualitative indicator for PMj5 with data of two dates on a normal day and low mobility associated to the quarantine of the Bogota mayor's office by Covid-19. For the data of the two dates, correlations were found between the AOD and the PM25 of 0.60 and 0.62. Interpolation maps were made with the data for PM25, which gave acceptable results. © 2022, Universidad Nacional de Colombia. All rights reserved.