Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections.

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases.

Transition to clean household energy through an application of integrated model: Ensuring sustainability for better health, climate and environment.

Sustained use and adoption of clean cooking fuels have become an important concern for developing countries due to the enormous burden of diseases attributable to household air pollution (HAP). The transition and adoption of clean household energy involve various socio-economic, behavioral, and technological barriers at different community levels. Hence, the present paper aims to scrutinize the factors, key determinants, and other interventions among rural households that limit clean cookstoves' sustained uses. The study proposes an integrated model to enhance clean cooking fuel uptake and uses based on the available evidence. The health, climate and environmental factors were identified as the key to trigger the adoption of clean cooking fuel alternatives. The model comprises the integration of components for targeted clean fuel policy interventions and promotes green recovery. The elements include Knowledge, Housing characteristics, Awareness, Interventions, Willingness to pay, Adoption, Lower emissions and Gender Equality (THE KHAIWAL model) to ascertain the intervention focus regions. Integration of model components in policy implementation will promote clean household energy to reduce emissions, leading to improve quality of life, good health, women empowerment, better air quality and climate.

Impact of the COVID-19 pandemic on clean fuel programmes in India and ensuring sustainability for household energy needs.

Clean cooking energy strategies are critical for reducing air pollution, improving health, and achieving related Sustainable Development Goals. The recent COVID-19 lockdowns may impact the transition towards clean cooking fuels. The nationwide lockdown is likely to affect key factors such as energy access, income, transportation, etc., that play a role in decisions influencing household fuel use. The rural population already bears the burden of poverty and may not be able to afford and access clean cooking fuels during the lockdown. They are thus vulnerable to reversion to their traditional cooking methods using solid biomass fuels. The household air pollution caused due to the use of polluting fuels increases their susceptibility to non-communicable diseases, and thus may intensify the risk and severity of COVID-19 infection. Hence, there is an urgent need to expand sustainable energy solutions worldwide. The present study applies the DPSIR modeling framework to establish a set of comprehensive indicators for addressing the transition towards clean cooking fuels during the COVID-19 pandemic. The study also provides insights on various strategies adopted in India in response to the COVID-19 pandemic for maintaining continuity of delivering benefits under a clean cookstove program. The study offers future directions to ensure the transition towards cleaner fuels and sustainability.

Detection and identification of dust mite allergens in the air conditioning filters in Chandigarh, India.

Modern lifestyle with the increasing use of air conditioner (AC) has been linked with breathing difficulties, irritation, dryness, and other symptoms. Hence, dust mites were isolated from AC filters, which causes allergic diseases. A total of 95 dust samples were collected from AC filters from hospitals, guest house, office, school, and homes in Chandigarh, India. The highest concentration of dust mites was detected from hospitals (9/g), offices (7/g), households (6/g), guest houses (3/g), and schools (0/g). Based on the morphology of dust mites observed under a light microscope, Dermatophagoides and Acarus species were found most common. Indoor air quality was also monitored to find out their relation with dust mites present in AC filters. Further, the respiratory health status of indoor facility users was also assessed using a standard questionnaire as a study tool. It was seen that 55.3% of male among the total respondents were having an allergy and only 44.7% of the females had an allergy. The allergy among the male respondents (55.3%) was significantly more (p < 0.05) in comparison with female respondents (44.7%). Some of the respondents also reported a family history of rhinitis (31.9%), asthma (12.8%), recurrent urticaria (6.4%), and conjunctivitis (6.4%). Interestingly, 23.4% of study participants reported that they get disturbed by the use of AC and house dust was found to be the most triggering factor in enhancing the symptoms of allergy. Thus, it is recommended that air conditioner filters should be cleaned regularly to prevent the accumulation of the dust mites and related allergens on filter dust.

Appraisal of thermal comfort in rural household kitchens of Punjab, India and adaptation strategies for better health.

There is increasing evidence of adverse health impact of solid biomass fuel, and its use may hinder thermal comfort, which may lead to lower quality of life. Hence, current study aims to assess the thermal comfort at a rural location of Punjab, India. The indoor air temperature and relative humidity in rural households during winter varied from 11.9-25.2 °C and 63.4-90.5% respectively, during pre-summer it ranged between 21.3 and 27.4 °C and 48.4-78.4% while during summer it ranged between 28.4 and 37.8 °C and 13.7-63.8% respectively. The PMV of the households ranged between -0.85 to 0.69 (winter), -0.32 to 0.4 (pre-summer) and 0.53 to 1.25 (summer) for American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 55-2017 and -0.56 to 1.11 (winter), 0.04 to 0.99 (pre-summer) and 1.21-2.36 (summer) for European Committee for Standardization (CEN) European standard EN15251 while the Predicted Percentage of Dissatisfied ranged between 5 and 20% (winter), 5-8% (pre-summer) and 11-38% (summer) for ASHRAE 55-2017 and 5-31% (winter), 5-26% (pre-summer) and 36-90% (summer) for EN15251 guidelines. On the other hand, Adaptive thermal comfort (ATC) during winter and pre-summer was comfortable for 80 and 90% acceptable limits (ASHRAE-2017) and ranged between too cool to comfortable for EN15251 (Class I, II and III) while during summer thermal comfort for occupants was comfortable for ASHRAE 2017 and EN15251 (Class I, II, III) but did not comply with EN guidelines in some households using either clean fuel or chullah. Thermal comfort sensation was observed to be slightly cool to neutral during winter, neutral during pre-summer and slightly warm during summer according to Predicted Mean Vote method. The results were also compared using a thermal comfort and household survey and found to be similar with the model results. Climate change is leading to changes in temperature which may have an impact on the built environment. Hence, the current study suggests formulating policies on the uses of household fuel and design of kitchen with proper ventilation to increase thermal comfort which in turn will also reduce air pollutants.