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Brain tumors are one of the main worldwide causes of mortality and morbidity and a critical issue in health risk. Tumor growth prediction is a proper method for better understanding the phenomena and choosing the appropriate therapy for patients. Since tumors' physiological and morphological properties vary significantly in different individuals, using patient specific data is valuable for modelling tumor growth in staging and personalized-therapy planning. However, the validity of the models should be evaluated for their precision assessment based on the decision criteria. There are different sources of uncertainties affecting model prediction accuracy and decision making for the therapy. In this paper, an image-based tumor growth model is evaluated by taking into account uncertainties in the model parameters. The proposed reaction-diffusion model integrates cancerous cell proliferation and invasion through reaction and diffusion terms, respectively. Uncertainties in diffusion and proliferation coefficients were analyzed through Monte Carlo simulation. The time needed for tumor to grow to its fatal size was estimated through numerical solution of the model. Comparison of the predicted time distribution with and without considering uncertainties in model parameters shows a decrease in dispersity of predicted data that highlights the importance of uncertainty. Also, the wide range for survival time shows the importance of choosing proper parameters in order to enhance model accuracy. The recommendations were made for increasing the validity of the tumor growth models. [ABSTRACT FROM AUTHOR]

The mean mass concentrations of black carbon (BC), biomass burning (BC)bb, and fossil fuel combustion (BC)ff have been estimated during March–May 2020 (during the COVID-19 outbreak) and March–May 2019 at a semiarid region of Agra over the Indo-Gangetic basin region. The daily mean mass concentration of BC in 2020 and 2019 was 3.9 and 6.9 µg m−3, respectively. The high monthly mean mass concentration of BC was found to be 4.7, 3.4 and 3.3 µg m−3 in Mar-2020, Apr-2020, and May-2020, respectively, whereas in Mar-2019, Apr-2019, and May-2019 was 7.7, 7.5 and 5.4 µg m−3, respectively. The absorption coefficient (babs) and absorption angstrom exponent (AAE) of black carbon were calculated. The highest mean AAE was 1.6 in the year 2020 (Mar–May 2020) indicating the dominance of biomass burning. The mean mass concentration of fossil fuel (BC)ff and biomass burning (BC)bb is 3.4 and 0.51 µg m−3, respectively, in 2020 whereas 6.4 and 0.73 µg m−3, respectively, in 2019. The mean fraction contribution of BC with fossil fuel (BC)ff was 82.1 ± 13.5% and biomass burning (BC)bb was 17.9 ± 4.3% in 2020, while in 2019, fossil fuel (BC)ff was 86.7 ± 13.5% and biomass burning (BC)bb was 13.3 ± 6.7%. The population-weighted mean concentration of BC, fossil fuel (BC)ff, and biomass burning (BC)bb has been calculated. The health risk assessment of BC has been analyzed in the form of attributable relative risk factors and attributed relative risk during the COVID-19 outbreak using AirQ + v.2.0 model. The attributable relative risk factors of BC were 20.6% in 2020 and 29.4% in 2019. The mean attributed relative risk per 10,000,000 populations at 95% confidence interval (CI) due to BC was 184.06 (142.6–225.2) in 2020 and 609.06 (418.3–714.6) in 2019. The low attributed factor and attributed relative risk in 2020 may be attributed to improvements in air quality and a fall in the emission of BC. In 2020, due to the COVID-19 pandemic, the whole country faced the biggest lockdown, ban of the transportation of private vehicles, trains, aircraft, and construction activities, and shut down of the industry leading to a fall in the impact of BC on human health. Overall, this was like a blessing in disguise. This study will help in future planning of mitigation and emission control of air pollutants in large and BC in particular. It only needs a multipronged approach. This study may be like torch bearing to set path for mitigation of impacts of air pollution and improvement of air quality. [ABSTRACT FROM AUTHOR]

This study enhances the understanding of the particulate matters (PM2.5 and PM10) and their physical and chemical behavior over the Taj Mahal, Agra, in North-Central India. The mass concentration was determined, and the shape and size of the particles and chemical characterizations have been carried out using SEM–EDX. The high level and significant variation of PM10 (162.2 µg m−3) and PM2.5 (83.9 µg m−3) were observed. The exceedance factor of the present study region is in critical and moderate condition. Morphological characterization reveals the particles of different shapes and sizes, while elemental analysis shows the presence of Si, Al, Fe, Ca, K, Cl, Mg, Na, Cu, and Zn. The dominance of Si indicated the contribution of natural sources, i.e., soil over this region. Three significant sources, viz. soil/road paved dust/vegetative emissions, vehicular/industrial emissions, and intermingling of dust and combustion particles, have been identified using principal component analysis over North-Central India. Health risk analysis of particulate matter identified carcinogenic and non-carcinogenic metals in the present study, which comes in contact with human beings during inhalation. The non-carcinogenic risk was much higher than the acceptable level. The high carcinogenic risks were found in Zn in PM10 and Cu in PM2.5 for both children and adults. [ABSTRACT FROM AUTHOR]

Particulate-bound poly-aromatic hydrocarbons (PAHs) are of great concern due to their mutagenicity and carcinogenicity effect on human health. In this context, identification, quantification and inhalation cancer risk (ICR) assessment due to PM10- and PM2.5-bound PAHs has been carried out at six monitoring stations in a critically polluted Jharia coalfield/Dhanbad City. Identification of pollution sources at study area has been performed by using PCA statistical methods. Air quality index (AQI) and air quality health index (AQHI) were calculated based on the concentration levels of PM10. Location-wise direct comparison between AQI, AQHI and ICR was performed to analyse the risk levels. Consequently, maximum concentration levels of particulate (PM2.5 and PM10)-bound total PAHs (400 and 482 ng/m3) were recorded at the monitoring station Lodna Thana, followed by Bank More and Sijua Stadium, respectively. It was also observed that mine fire-affected station Lodna Thana was exaggerated with presence of PAHs due to wood and open coal burning activities. Moreover, about 1000 and 889 cases of inhalation cancer risk were estimated due to direct exposure of PM10- and PM2.5-bound PAHs in the study area, respectively. Active mine fire-affected station Lodna Thana was recorded with maximum probability of lung tumour due to inhalation cancer risk. This study has reported higher AQHI at station Dugdha Basti, Lodna Thana and Bank More, which results increased number of tumours due to ICR. This result concludes that Jharia coalfield/Dhanbad City are not only critically polluted area but it is also an inhalation cancer prone area due to direct exposure of active mine fire. [ABSTRACT FROM AUTHOR]