I am a PhD student working on atmospheric aerosol and gases, pollutant emission monitoring using RS & models. Supervisors: Jason Blake Cohen and Qin Kai
Direct radiative forcing (DRF) of aerosols is driven by aerosol concentration, size, and mixing s... more Direct radiative forcing (DRF) of aerosols is driven by aerosol concentration, size, and mixing state, and solar radiation. This work introduces Core-Shell Mie model optimization (COSMO) to compute top of the atmosphere (TOA) forcing based on inversely constrained black carbon (BC) size and mixing state from AERONET, over two rapidly developing areas: Lumbini and Taihu. COSMO has both, a less negative TOA than AERONET and a wider range of variability, with the mean and standard deviation difference between COSMO and AERONET being 13 ± 8.1 W m −2 at Lumbini and 16 ± 12 W m −2 at Taihu. These differences are driven by particle aging and size-resolved BC emissions, with up to 17.9% of cases warmer than the maximum AERONET TOA, and 1.9% of the total possible cases show a net-warming at TOA (TOA > 0). A linearized correction is deduced which can be immediately implemented by climate models, and suggested ranges of BC size and mixing observations are made for future campaigns. Given that the COSMO TOA bias and uncertainty are larger than the forcing of locally emitted GHGs, active consideration of BC is necessary to reduce climate uncertainty in developing areas.
Rapid industrialization and urbanization have caused frequent haze pollution episodes during wint... more Rapid industrialization and urbanization have caused frequent haze pollution episodes during winter in eastern China. Considering that the vertical profile of the aerosol properties changes significantly with altitude, investigating aerosol aloft information via satellite remote sensing is essential for studying regional transport, climate radiative effects, and air quality. Through a synergic approach between lidar, the AErosol RObotic NETwork sunphotometer observations, and WRF-Chem simulations, several transboundary aloft transport events of haze aerosols to Xuzhou, eastern China, are investigated in terms of source, type, and composition and the impact on optical properties. Upper-air aerosol layers are short-lived tiny particles that increase the total aerosol optical depth (AOD). The aloft aerosols not only play a critical role during the haze event, enhancing the scattering of aerosol particles significantly but also cause a rise in the AOD and the Ångström exponent (AE), which increases the proportion of fine particles, exacerbating the pollution level near the surface. Based on the model simulation results, our study highlights that the transported aloft aerosols lead to the rapid formation of secondary inorganic substances, such as secondary sulfates, nitrates, and ammonium salts, which strongly contribute to haze event formation. Moreover, the results provide evidence that the haze frequency events associated with polluted dust outbreaks were higher for 2014–2015 winter. A closer analysis shows that the advected dust layers over Xuzhou originated from Inner Mongolia and the Xinjiang Uygur Autonomous Region. The study of the occurrence frequency, height, thickness, and optical properties of aloft anthropogenic haze in China will further deepen our understanding and provide a strong basis to assess aerosol impact on transport and the Earth–atmosphere radiative balance.
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
This study investigates spatiotemporal changes in air pollution (particulate as well as gases) du... more This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, PM2.5 and PM10 from Copernicus Atmosphere Monitoring Service (CAMS), and NO2 and O3 from TROPOMI-5P, from March to June 2019–2020. Additionally, aerosol subtypes from the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) were used to explore the aerosol types. The strict lockdown (26 March–30 May 2020) led to a significant reduction in AOD (up to 47%) in all major cities, while the partial lockdown (June 2020) led to increased and decreased AOD over the study area. Significant reductions in PM2.5 (37–77%) and PM10 (33–70%) were also observed throughout the country during the strict lockdown and partial lockdown. The NO2 levels decreased by 3–25% in March 2020 in the c...
Rainfall is the most important meteorological variable that influences the economic development o... more Rainfall is the most important meteorological variable that influences the economic development of Rwanda. Changes in rainfall trends and variability over recent past years have become a great concern to policymakers and scientists. This study aims at examining the spatiotemporal variability of rainfall over Rwanda and the teleconnections of rainfall with different large-scale ocean-atmospheric variables at different timescales. The study used rainfall data of Climate
This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODI... more This letter reports uncertainties in the Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 dark target (DT), deep blue (DB), and multiangle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD) during the COVID-19 lockdown period (February-May 2020) compared to the pre-COVID-19 period (February-May 2019). Validation of AOD retrievals was conducted against AErosol RObotic NETwork (AERONET) Version 3 Level 1.5 AOD data obtained from three sites located in urban (Beijing_CAMS and Beijing_RADI) and suburban (XiangHe) areas of China. The results show the poor performance of the DT and DB algorithms compared to the MAIAC algorithm, which performed better during the lockdown period. Overall, all MODIS algorithms overestimated the AOD and showed higher positive bias under high aerosol loading conditions during lockdown than during prelockdown. This is mainly attributed to the overestimation of the aerosol single-scattering albedo (SSA), which was found higher during lockdown than during the same period in 2019.
This paper reports a study on the statistics for particulate matter pollution (PM 2.5) and the CO... more This paper reports a study on the statistics for particulate matter pollution (PM 2.5) and the COVID-19 lockdown in the Kathmandu valley. The PM 2.5 decreased during the COVID-19 pandemic lockdown periods 2020 compared to the average value of the previous three years (2017, 2018, and 2019). Further, analysis of active fire and air mass trajectory for April and May in 2019 and 2020 shows that the particulate matter trend associated with Kathmandu is not directly influenced by the long-range transport of wind carrying aerosols from the active fire regions. Statistical tests indicate a reduction of particulate matter pollution during the period.
This study investigates spatiotemporal changes in air pollution (particulate as well as gases) du... more This study investigates spatiotemporal changes in air pollution (particulate as well as gases) during the COVID-19 lockdown period over major cities of Bangladesh. The study investigated the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites, PM2.5 and PM10 from Copernicus Atmosphere Monitoring Service (CAMS), and NO2 and O3 from TROPOMI-5P, from March to June 2019-2020. Additionally, aerosol subtypes from the Cloud-Aerosol Lidar and Infrared Pathfinder (CALIPSO) were used to explore the aerosol types. The strict lockdown (26 March-30 May 2020) led to a significant reduction in AOD (up to 47%) in all major cities, while the partial lockdown (June 2020) led to increased and decreased AOD over the study area. Significant reductions in PM2.5 (37-77%) and PM10 (33%-70%) were also observed throughout the country during the strict lockdown and partial lockdown. The NO2 levels decreased by 3%-25% in March 2020 in the cities of Rajshahi, Chattogram, Sylhet, Khulna, Barisal, and Mymensingh, in April by 3%-43% in Dhaka, Chattogram, Khulna, Barisal, Bhola, and Mymensingh, and May by 12%−42% in Rajshahi, Sylhet, Mymensingh, and Rangpur. During the partial lockdown in June, NO2 decreased (9%−35%) in Dhaka, Chattogram, Sylhet, Khulna, Barisal, and Rangpur compared to 2019. On the other hand, increases were observed in ozone (O3) levels, with an average increase of 3%-12% throughout the country.
Automatic Classification Approach for Detecting COVID-19 using Deep Convolutional Neural Networks
The COVID-19 pandemic situation has created even more difficulties in the quick identification an... more The COVID-19 pandemic situation has created even more difficulties in the quick identification and screening of the COVID-19 patients for the medical specialists. Therefore, a significant study is necessary for detecting COVID-19 cases using an automated diagnosis method, which can aid in controlling the spreading of the virus. In this paper, the study suggests a Deep Convolutional Neural Network-based multi-classification approach (COV-MCNet) using eight different pre-trained architectures such as VGG16, VGG19, ResNet50V2, DenseNet201, InceptionV3, MobileNet, InceptionResNetV2, Xception which are trained and tested on the X-ray images of COVID-19, Normal, Viral Pneumonia, and Bacterial Pneumonia. The results from 3-class (Normal vs. COVID-19 vs. Viral Pneumonia) showed that only the ResNet50V2 model provides the highest classification performance (accuracy: 95.83%, precision: 96.12%, recall: 96.11%, F1-score: 96.11%, specificity: 97.84%) compared to rest of the models. The results ...
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Papers by Pravash Tiwari