Estimating black carbon levels using machine learning models in high-concentration regions.

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Physico-Chemical Properties and Deposition Potential of PM2.5 during Severe Smog Event in Delhi, India.

The present work studies a severe smog event that occurred in Delhi (India) in 2017, targeting the characterization of PM2.5 and its deposition potential in human respiratory tract of different population groups in which the PM2.5 levels raised from 124.0 µg/m3 (pre-smog period) to 717.2 µg/m3 (during smog period). Higher concentration of elements such as C, N, O, Na, Mg, Al, Si, S, Fe, Cl, Ca, Ti, Cr, Pb, Fe, K, Cu, Cl, P, and F were observed during the smog along with dominant organic functional groups (aldehyde, ketones, alkyl halides (R-F; R-Br; R-Cl), ether, etc.), which supported potential contribution from transboundary biomass-burning activities along with local pollution sources and favorable meteorological conditions. The morphology of individual particles were found mostly as non-spherical, including carbon fractals, aggregates, sharp-edged, rod-shaped, and flaky structures. A multiple path particle dosimetry (MPPD) model showed significant deposition potential of PM2.5 in terms of deposition fraction, mass rate, and mass flux during smog conditions in all age groups. The highest PM2.5 deposition fraction and mass rate were found for the head region followed by the alveolar region of the human respiratory tract. The highest mass flux was reported for 21-month-old (4.7 × 102 µg/min/m2), followed by 3-month-old (49.2 µg/min/m2) children, whereas it was lowest for 21-year-old adults (6.8 µg/min/m2), indicating babies and children were more vulnerable to PM2.5 pollution than adults during smog. Deposition doses of toxic elements such as Cr, Fe, Zn, Pb, Cu, Mn, and Ni were also found to be higher (up to 1 × 10-7 µg/kg/day) for children than adults.

Impact of Chemical Properties of Human Respiratory Droplets and Aerosol Particles on Airborne Viruses' Viability and Indoor Transmission.

The airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been identified as a potential pandemic challenge, especially in poorly ventilated indoor environments, such as certain hospitals, schools, public buildings, and transports. The impacts of meteorological parameters (temperature and humidity) and physical property (droplet size) on the airborne transmission of coronavirus in indoor settings have been previously investigated. However, the impacts of chemical properties of viral droplets and aerosol particles (i.e., chemical composition and acidity (pH)) on viability and indoor transmission of coronavirus remain largely unknown. Recent studies suggest high organic content (proteins) in viral droplets and aerosol particles supports prolonged survival of the virus by forming a glassy gel-type structure that restricts the virus inactivation process under low relative humidity (RH). In addition, the virus survival was found at neutral pH, and inactivation was observed to be best at low (<5) and high pH (>10) values (enveloped bacteriophage Phi6). Due to limited available information, this article illustrates an urgent need to research the impact of chemical properties of exhaled viral particles on virus viability. This will improve our fundamental understanding of indoor viral airborne transmission mechanisms.

Five probable factors responsible for the COVID-associated mucormycosis outbreak in India.

The second wave of COVID-19 due to the delta variant (B.1.617.2) led to a rapid rise in total coronavirus and COVID-associated mucormycosis cases reported from India. Hence, our study explored the possible causes of a rapid upsurge in COVID-associated mucormycosis, which has accounted for over 70% of global cases. Factors associated with the increase in mucormycosis cases in COVID-19 patients include diabetes mellitus, steroid overdose, high iron levels, and immunosuppression, combined with other possible factors, such as unhygienic conditions, prolonged hospitalization, use of ventilators, and leaky humidifiers in oxygen cylinders. These create an ideal environment for contracting mucormycosis. However, these cases could be reduced by disseminating simple preventive measures and creating awareness among the medical society and general public of this rare and deadly contagion of COVID-associated mucormycosis. The identification of early symptoms will also help to restrict the spread of lethal fungal diseases. Furthermore, a collaborative team of surgeons, ophthalmologists, physicians, and otolaryngologists would be required in the hospital wards to accelerate surgeries on severely impacted patients.

Preventing Airborne Transmission of SARS-CoV-2 in Hospitals and Nursing Homes.

The first case of the coronavirus disease 2019 (COVID-19), the novel contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was reported in Wuhan, China in December 2019 [...].

Influence of chemical aging on physico-chemical properties of mineral dust particles: A case study of 2016 dust storms over Delhi.

The physico-chemical properties of dust particles collected During Dust Storm (DDS) and After Dust Storm (ADS) events were studied using Scanning Electron Microscope coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS), X-ray Fluorescence Spectroscopy (XRF) and X-ray Photoelectron Spectroscopy (XPS). Morphological and compositional change in dust particles were observed as they react with the anthropogenic pollutants present in the urban environment. The calcite rich particles were observed to transform into calcium chloride, calcium nitrate, and calcium sulfate on reacting with the chlorides, nitrates, and sulfates present in the urban atmosphere. The frequency distributions of Aspect Ratio (AR) for the DDS and ADS particles were observed to be bimodal (mode peaks at 1.2 and 1.5) and monomodal (mode peak at 1.1), respectively. The highly irregular shaped solid dust particles were observed to transform into nearly spherical semisolid particles in the urban environment. XPS analysis confirms the high concentration of oxides, nitrates, and chlorides at the surface of ADS samples which show the signatures of mineral dust particles aging. Species with a high value of imaginary part of refractive index (like Cr metal, Fe metal, Cr2O3, FeO, Fe2O3) were observed at the surface of dust particles. At 550 nm wavelength, the light-absorbing potential of the observed species along with black carbon (BC) was found to vary in the order; Cr metal > Fe metal > Cr2O3> FeO > BC > Fe2O3> FeOOH. The presence of the aforementioned species on the surface of ADS particles will tremendously affect the particle optical and radiative properties compared to that of DDS particles. The present work could reduce the uncertainty in the radiation budget estimations of mineral dust and assessment of their climatic impacts over Delhi.