Association of air quality with respiratory and cardiovascular morbidity rate in Delhi, India

Modulation of miR-466d-3p on Wnt signaling pathway in response to DEPs-induced blood-brain barrier disruption

BACKGROUND

Diesel exhaust particles (DEPs), a predominant component of ambient particulate matter (PM), are classified as ultrafine particles with the capacity to penetrate the cerebral blood-brain barrier (BBB). This penetration is implicated in the pathogenesis of central nervous system (CNS) disorders. The integrity of the BBB is inextricably linked to cerebrovascular homeostasis and the development of neurodegenerative disease, highlighting the importance of studying the effects and mechanisms of DEPs on BBB function damage.

METHODS AND RESULTS

Utilizing mouse cerebral microvascular endothelial cells (bEnd.3 cells) as an in vitro model of the BBB, we explored the detrimental effects of DEPs exposure on BBB permeability and integrity, with particular focus on inflammation, cell apoptosis, and miRNA expression profiles. Our findings revealed that exposure to DEPs at varying concentrations for 48 h resulted in the inhibition of bEND.3 cell proliferation, induction of cell apoptosis, and an upregulation in the secretion of inflammatory cytokines/chemokines and adhesion molecules. The BBB integrity was further compromised, as evidenced by a decrease in trans-epithelial electrical resistance(TEER), a reduction in cytoskeletal F-actin, and diminished tight junction (TJ) protein expression. Microarray analysis revealed that 23 miRNAs were upregulated and 11 were downregulated in response to a 50 μg/mL DEPs treatment, with miR-466d-3p being notably differentially expressed. Wnt3 was identified as a target of miR-466d-3p, with the Wnt signaling pathway being significantly enriched. We validated that miR-466d-3p expression was downregulated, and the protein expression levels of Wnt/β-catenin and Wnt/PCP signaling components were elevated. The modulation of the Wnt signaling pathway by miR-466d-3p was demonstrated by the transfection of miR-466d-3p mimic, which resulted in a downregulation of Wnt3 and β-catenin protein expression, and the mRNA level of Daam1, as well as an enhancement of TJ proteins ZO-1 and Claudin-5 expression.

CONCLUSIONS

Our study further confirmed that DEPs can induce the disruption of BBB integrity through inflammatory processes. We identified alterations in the expression profile of microRNAs (miRNAs) in endothelial cells, with miR-466d-3p emerging as a key regulator of tight junction (TJ) proteins, essential for maintaining BBB integrity. Additionally, our findings primarily demonstrated that the Wnt/ β-catenin and Wnt/PCP signaling pathway can be activated by DEPs and are regulated by miR-466d-3p. Under the combined effects of Wnt/PCP and inflammation, there is an ultimate increase in BBB hyperpermeability.

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India

This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum cofficient of determination (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.

Black Phosphorus-Tungsten Oxide Sandwich-like Nanostructures for Highly Selective NO2 Detection

Modern chemical production processes often emit complex mixtures of gases, including hazardous pollutants such as NO2. Although widely used, gas sensors based on metal oxide semiconductors such as WO3 respond to a wide range of interfering gases other than NO2. Consequently, developing WO3 gas sensors with high NO2 selectivity is challenging. In this study, a simple one-step hydrothermal method was used to prepare WO3 nanorods modified with black phosphorus (BP) flakes as sensitive materials for NO2 sensing, and BP-WO3-based micro-electromechanical system gas sensors were fabricated. The characterization of the as-prepared BP-WO3 composite through X-ray diffraction scanning electron microscopy and X-ray photoelectron spectroscopy confirmed the successful formation of the sandwich-like nanostructures. The result of gas-sensing tests with 2-14 ppm NO2 indicated that the sensor response was 1.25-2.21 with response-recovery times of 36 and 36 s, respectively, at 190 °C. In contrast to pure WO3, which exhibited a response of 1.07-2.2 to 0.3-5 ppm H2S at 160 °C, BP-WO3 showed almost no response to H2S. Thus, compared with pure WO3, BP-WO3 exhibited significantly improved NO2 selectivity. Overall, the BP-WO3 composite with sandwich-like nanostructures is a promising material for developing highly selective NO2 sensors for practical applications.

Effects of Ambient Air Pollutants on Hospital Admissions among Children Due to Asthma and Wheezing-Associated Lower Respiratory Infections in Mysore, India: A Time Series Study

Air pollutants are known to trigger asthma and wheezing-associated lower respiratory infections in children, but evidence regarding their effect on hospital admissions in India is limited. We conducted a time-series study over a period of five years to assess the role of ambient air pollutants in daily asthma-related hospital admissions in children in Mysore, India. Daily asthma and wheeze (associated with lower respiratory infections) admissions were modelled using a generalised additive model (GAM) to examine the non-linear effects and generalised linear models (GLM) for linear effects, if any. Models were adjusted by day of the week and lag days, with smooth terms for time, maximum temperature, and relative humidity, and they were stratified by sex and age group. Of the 362 children admitted, more than 50% were boys, and the mean age was 5.34 years (±4.66). The GAMs showed non-linear associations between NO2, PM2.5, and NH3. For example, a 10 µgm-3 (or 10%) increase in NO2 increased admissions by 2.42. These non-linear effects were more pronounced in boys. A linear effect was detected for PM10 with a relative risk (95% CI) of 1.028, 1.013, and 1.043 with admission. Further research is needed to explore whether these findings can be replicated in different cities in India. Air pollution needs to be controlled, and policies that focus on lower cut-off levels for vulnerable populations are necessary.

Dispersion characteristics of bioaerosols during treatment of rural solid waste in northwest China

In rural China, the release of bioaerosols containing pathogens from solid waste dumps poses a potential health risk to the local population. Here, we sampled bioaerosols from rural solid waste-treatment in four provinces of northwest China to investigate their emission and dispersion characteristics in order to provide a scientific basis for control and risk reduction of bioaerosols released from rural sanitation facilities. The airborne bioaerosol concentrations and particle size distributions were calculated using an Anderson six-stage airborne microbial sampler and counting with its internal Petri dish culture. High-throughput sequencing was used to characterize the microbial composition at different sampling sites and to explore possible influencing factors, while the health risk associated with exposure was estimated based on average daily dose-rate. The highest concentration point values of bacteria and fungi in bioaerosols near the solid waste were 63,617 ± 15,007 and 8044 ± 893 CFU/m³, respectively. Furthermore, the highest concentration point values of Enterobacteriaceae was 502 ± 35 CFU/m³. Most bioaerosols were coarse particles larger than 3.3 μm. Potentially pathogenic genera of winter-indicator species detected in the air were primarily Delftia, Rhodococcus and Aspergillus. The composition of solid waste and environmental conditions are important factors in determining the characteristics of bioaerosols. Local residents are exposed to bioaerosols mainly through inhalation. Children are at a particularly high risk of exposure through both inhalation and skin contact. The results of this study show that bioaerosols in the vicinity of rural solid waste dumps pose a health risk to the surrounding population. More suitable risk assessment criteria for rural areas should be established, and corresponding control and protection measures should be taken from three aspects: generation source and transmission pathway, as well as the recipient.

Air pollution and its effects on emergency room visits in tertiary respiratory care centers in Delhi, India

Environmental pollution has harmful effects on human health, particularly the respiratory system. We aimed to study the impact of daily ambient air pollution on daily emergency room visits for acute respiratory symptoms. This study was conducted in two tertiary respiratory care centres in Delhi, India. Daily counts of emergency room visits were collected. All patients attending the emergency room were screened for acute onset (less than 2 weeks) of respiratory symptoms and were recruited if they were staying in Delhi continuously for at least 4 weeks and having onset (≤2 weeks) of respiratory symptoms. Daily average air pollution data for the study period was obtained from four continuous ambient air quality monitoring stations. A total of 61,285 patients were screened and 11,424 were enrolled from June 2017 to February 2019. Cough and difficulty in breathing were most common respiratory symptoms. Poor air quality was observed during the months of October to December. Emergency room visits with acute respiratory symptoms significantly increased per standard deviation increase in PM10 from lag days 2-7. Increase in wheezing was primarily seen with increase in NO2. Pollutant levels have effect on acute respiratory symptoms and thus influence emergency room visits. *************************************************************** *Appendix Authors list Kamal Singhal,1 Kana Ram Jat,2 Karan Madan,3 Mohan P. George,4 Kalaivani Mani,5 Randeep Guleria,3 Ravindra Mohan Pandey,5 Rupinder Singh Dhaliwal,6 Rakesh Lodha,2 Varinder Singh1 1Department of Paediatrics, Lady Hardinge Medical College and associated Kalawati Saran Children's Hospital, New Delhi, India 2Department of Paediatrics, All India Institute of Medical Sciences, New Delhi, India 3Department of Pulmonary Medicine, Critical Care and Sleep Disorders, All India Institute of Medical Sciences, New Delhi, India 4Department of Environment, Delhi Pollution Control Committee, Kashmere Gate, New Delhi, India 5Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India 6Department of Non-communicable Diseases, Indian Council of Medical Research, New Delhi, India.

Health and economic impacts of ambient air pollution on hospital admissions for overall and specific cardiovascular diseases in Panzhihua, Southwestern China

Background

The associations of ambient air pollution with hospital admissions (HAs) for overall and specific causes of cardiovascular diseases (CVDs), as well as related morbidity and economic burdens remain understudied, especially in low-pollution areas of low- and middle-income countries (LMICs). We evaluated the short-term effects of exposure to PM_2.5 (particles with an aerodynamic diameter ≤2.5 μm), PM₁₀ (particles with an aerodynamic diameter ≤10 μm), and SO₂ (sulfur dioxide) on HAs for CVDs in Panzhihua, China, during 2016-2020, and calculated corresponding attributable risks and economic burden.

Methods

We used a generalized additive model (GAM) while controlling for time trends, meteorological conditions, holidays, and days of the week to estimate the associations. The cost of illness (COI) method was adopted to further assess corresponding hospitalization costs and productivity losses.

Results

A total of 27 660 HAs for CVDs were included in this study. PM₁₀ and SO₂ were significantly associated with elevated risks of CVDs hospitalizations. Each 10 μg/m³ increase in PM₁₀ and SO₂ at lag06 corresponded to an increase of 2.48% (95% confidence interval (CI) = 0.92%-4.06%), and 5.50% (95% CI = 3.09%-7.97%) in risk of HAs for CVDs, respectively. The risk estimates of PM₁₀ and SO₂ on CVD hospitalizations were generally robust after adjustment for other pollutants in two-pollutant models. We found stronger associations between air pollution (PM₁₀ and SO₂) and CVDs in cool seasons than in warm seasons. For specific causes of CVDs, significant associations of PM₁₀ and SO₂ exposure with cerebrovascular disease and ischaemic heart disease were observed. Using 0 μg/m³ as the reference concentrations, 11.91% (95%CI = 4.64%-18.56%) and 15.71% (95%CI = 9.30%-21.60%) of HAs for CVDs could be attributable to PM₁₀ and SO₂, respectively. During the study period, PM₁₀ and SO₂ brought 144.34 million Yuan economic losses for overall CVDs, accounting for 0.028% of local GDP.

Conclusions

Our results suggest that PM₁₀ and SO₂ exposure might be an important trigger of HAs for CVDs and accounted for substantial morbidity and economic burden.

A review of respirable fine particulate matter (PM2.5)-induced brain damage

Respirable fine particulate matter (PM_2.5) has been one of the most widely publicized indicators of pollution in recent years. Epidemiological studies have established a strong association between PM_2.5, lung disease, and cardiovascular disease. Recent studies have shown that PM_2.5 is also strongly associated with brain damage, mainly cerebrovascular damage (stroke) and neurological damage to the brain (changes in cognitive function, dementia, psychiatric disorders, etc.). PM_2.5 can pass through the lung–gas–blood barrier and the “gut–microbial–brain” axis to cause systemic oxidative stress and inflammation, or directly enter brain tissue via the olfactory nerve, eventually damaging the cerebral blood vessels and brain nerves. It is worth mentioning that there is a time window for PM_2.5-induced brain damage to repair itself. However, the exact pathophysiological mechanisms of brain injury and brain repair are not yet fully understood. This article collects and discusses the mechanisms of PM_2.5-induced brain injury and self-repair after injury, which may provide new ideas for the prevention and treatment of cerebrovascular and cerebral neurological diseases.

Impact of air pollutants on COVID-19 transmission: a study over different metropolitan cities in India

India is affected strongly by the Coronavirus and within a short period, it becomes the second-highest country based on the infected case. Earlier, there was an indication of the impact of pollution on COVID-19 transmission from a few studies with early COVID-19 data. The study of the effect of pollution on COVID-19 in Indian metropolitan cities is ideal due to the high level of pollution and COVID-19 transmission in these cities. We study the impact of different air pollutants on the spread of coronavirus in different cities in India. A correlation is studied with daily confirmed COVID-19 cases with a daily mean of ozone, particle matter (PM) in size ≤ 10 μ m, carbon monoxide, sulfur dioxide, and nitrogen dioxide of different cities. It is found that particulate matter concentration decreases during the nationwide lockdown period and the air quality index improves for different Indian regions. A correlation between the daily confirmed cases with particulate matter (PM2.5 and PM10 both) is observed. The air quality index also shows a positive correlation with the daily confirmed cases for most of the metropolitan Indian cities. The correlation study also indicates that different air pollutants may have a role in the spread of the virus.

Generating Fine-Scale Aerosol Data through Downscaling with an Artificial Neural Network Enhanced with Transfer Learning

Spatially and temporally resolved aerosol data are essential for conducting air quality studies and assessing the health effects associated with exposure to air pollution. As these data are often expensive to acquire and time consuming to estimate, computationally efficient methods are desirable. When coarse-scale data or imagery are available, fine-scale data can be generated through downscaling methods. We developed an Artificial Neural Network Sequential Downscaling Method (ASDM) with Transfer Learning Enhancement (ASDMTE) to translate time-series data from coarse- to fine-scale while maintaining between-scale empirical associations as well as inherent within-scale correlations. Using assimilated aerosol optical depth (AOD) from the GEOS-5 Nature Run (G5NR) (2 years, daily, 7 km resolution) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) (20 years, daily, 50 km resolution), coupled with elevation (1 km resolution), we demonstrate the downscaling capability of ASDM and ASDMTE and compare their performances against a deep learning downscaling method, Super Resolution Deep Residual Network (SRDRN), and a traditional statistical downscaling framework called dissever ASDM/ASDMTE utilizes empirical between-scale associations, and accounts for within-scale temporal associations in the fine-scale data. In addition, within-scale temporal associations in the coarse-scale data are integrated into the ASDMTE model through the use of transfer learning to enhance downscaling performance. These features enable ASDM/ASDMTE to be trained on short periods of data yet achieve a good downscaling performance on a longer time-series. Among all the test sets, ASDM and ASDMTE had mean maximum image-wise R2 of 0.735 and 0.758, respectively, while SRDRN, dissever GAM and dissever LM had mean maximum image-wise R2 of 0.313, 0.106 and 0.095, respectively.

Source profiling of air pollution and its association with acute respiratory infections in the Himalayan-bound region of India

The studies related to air pollutants and their association with human health over the mountainous region are of utmost importance and are sparse especially over the Himalayan region of India. The linkages between various atmospheric variables and clinically validated data have been done using various datasets procured from satellite, model reanalysis, and surface observations during 2013-2017. Aerosol optical depth, air temperature, and wind speed are significantly related (p < 0.001) to the incidence of acute respiratory infections with its peak during winter. Model-derived particulate matter (PM_2.5) shows high contributions of black carbon, organic carbon, and sulfate during winter. The wind roses show the passage of winds from the south-west and southern side of the region. Back trajectory density plot along with bivariate polar plot analyses have shown that most of the winds coming from the western side are taking a southward direction before reaching the study area and may be bringing pollutants from the Indo-Gangetic Plain and other surrounding regions. Our study shows that the accumulation of pollutants in the Himalayan valley is owing to the meteorological stability with significant local emissions from burning of biomass and biofuels along with long-range and mid-range transport during the winter season that significantly correlated with the incidence of acute respiratory infections in the region.

Effects of ambient air pollution on emergency room visits of children for acute respiratory symptoms in Delhi, India

The present study explored the association between daily ambient air pollution and daily emergency room (ER) visits due to acute respiratory symptoms in children of Delhi. The daily counts of ER visits (ERV) of children (≤15 years) having acute respiratory symptoms were obtained from two hospitals of Delhi for 21 months. Simultaneously, data on daily concentrations of particulate matter (PM₁₀ and PM_2.5), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) and weather variables were provided by the Delhi Pollution Control Committee. K-means clustering with time-series approach and multi-pollutant generalized additive models with Poisson link function was used to estimate the 0-6-day lagged change in daily ER visits with the change in multiple pollutants levels. Out of 1,32,029 children screened, 19,120 eligible children having acute respiratory symptoms for ≤2 weeks and residing in Delhi for the past 4 weeks were enrolled. There was a 29% and 21% increase in ERVs among children on high and moderate level pollution cluster days, respectively, compared to low pollution cluster days on the same day and previous 1-6 days of exposure to air pollutants. There was percentage increase (95% CI) 1.50% (0.76, 2.25) in ERVs for acute respiratory symptoms for 10 μg/m³ increase of NO₂ on previous day 1, 46.78% (21.01, 78.05) for 10 μg/m³ of CO on previous day 3, and 13.15% (9.95, 16.45) for 10 μg/m³ of SO₂ on same day of exposure. An increase in the daily ER visits of children for acute respiratory symptoms was observed after increase in daily ambient air pollution levels in Delhi.

Exploring short term spatio-temporal pattern of PM2.5 and PM10 and their relationship with meteorological parameters during COVID-19 in Delhi

Present study aims to examine the impact of lockdown on spatio-temporal concentration of PM2.5 and PM10 - categorized and recorded based on its levels during pre-lockdown, lockdown and unlock phases while noting the relationship of these levels with meteorological parameters (temperature, wind speed, relative humidity, rainfall, pressure, sun hour and cloud cover) in Delhi. To aid the study, a comparison was made with the last two years (2018 to 2019), covering the same periods of pre-lockdown, lockdown and unlock phases of 2020. Correlation analysis, linear regression (LR) was used to examine the impact of meteorological parameters on particulate matter (PM) concentrations in Delhi, India. The findings showed that (i) substantial decline of PM concentration in Delhi during lockdown period, (ii) there were substantial seasonal variation of particulate matter concentration in city and (iii) meteorological parameters have close associations with PM concentrations. The findings will help planners and policy makers to understand the impact of air pollutants and meteorological parameters on infectious disease and to adopt effective strategies for future.

Contamination of obsterics and gynecology hospital air by bacterial and fungal aerosols associated with nosocomial infections

Bacterial and fungal bioaerosols are a global concern due to nosocomial infections, especially in developing countries. Our study aimed to detect fungal and bacterial bioaerosols in different wards of an obstetrics and gynecology hospital air samples. 240 bioaerosol samples were collected by performing impaction method from different wards of a hospital in the central part of Iran, during two seasons. Fungi genera and bacteria species are recognized by cultivation. Concentrations of bacteria and fungi were ranged from 44 to 75 CFU/m3 and 8 to 22 CFU/m3, respectively. Labor Delivery and Recovery (LDR) and Emergency room had first and second most contaminated air among all the hospital wards. No significant difference between microbial load of wards which used natural ventilation and heating, ventilating, and air conditioning (HVAC) system was observed. The microbial load was not affected significantly by temperature, working shift, and Inpatient Bed Occupancy Rate (IBOR). Fungal load related significantly with relative humidity. Staphylococcus aureus (detected in 48.3% of samples) and Penicillium (27%) were the most predominant isolated bacteria and fungi, respectively. The results revealed that the level of bacteria and fungi responsible for nosocomial infections in the air of this hospital is very low. Although levels of microbial contamination are relatively low, it is important to investigate the effect of bioaerosols on nosocomial infections, especially in neonates.

Examining the Relationships Between Air Pollutants and the Incidence of Acute Aortic Dissection with Electronic Medical Data in a Moderately Polluted Area of Northwest China

This paper explored whether air pollutants influenced acute aortic dissection (AAD) incidence in a moderately polluted area. A total of 494 AAD patients’ data from 2013 to 2016 were analyzed. The results showed that AAD had the strongest associations with PM10, SO2, NO2, CO, and O3 on the day before an AAD incident (lag1) and with PM2.5 two days before an incident (lag2) in single-pollutant model. In the three-pollutant model, PM10 was associated with the highest risk of adverse effects (RR = 1.37, 95% CI: 1.22, 1.53), whereas PM2.5 was associated with the lowest risk (RR = .83, 95% CI: .79, .88). Both PM2.5 and PM10 were affected by season, and SO2 was significantly different between heating and non-heating seasons as well. This study revealed significant associations between short-term PM2.5, PM10, and SO2 exposure and daily AAD incidence, showing that PM10 and SO2 were strong predictors of AAD incidence in a moderately polluted area.

Short and Long Term Exposure to Ambient Air Pollution and Impact on Health in India: A Systematic Review

Health effects attributable to short-term and long-term ambient air pollution (AAP) exposure in Indian population are less understood. This study evaluates the effect of short time and long-term exposure to AAP on respiratory morbidity, mortality and premature mortality for the exposed population. A total of 59 studies are reviewed to examine the effects of short-term exposure (n = 23); long-term exposure (n = 18) and premature mortality (n = 18). Short-term exposures to ambient pollutants have strong associations between COPD, respiratory illnesses and higher rates of hospital admission or visit. The long-term effects of AAP, associated with deficit lung function, asthma, heart attack, cardiovascular mortality and premature mortality have received much attention. Particulate matter (PM_2.5 and PM₁₀) is primarily responsible for respiratory health problems. Out of 18 literature reviewed on premature mortality, most (12 of 18) studies have statistically significant associations between AAP exposure and increased premature mortality risk.

Record high levels of atmospheric ammonia over India: Spatial and temporal analyses

Atmospheric ammonia (NH₃) is an alkaline gas and a prominent constituent of the nitrogen cycle that adversely affects ecosystems at higher concentrations. It is a pollutant, which influences all three spheres such as haze formation in the atmosphere, soil acidification in the lithosphere, and eutrophication in water bodies. Atmospheric NH₃ reacts with sulfur (SO_x) and nitrogen (NO_x) oxides to form aerosols, which eventually affect human health and climate. Here, we present the seasonal and inter-annual variability of atmospheric NH₃ over India in 2008-2016 using the IASI (Infrared Atmospheric Sounding Interferometer) satellite observations. We find that Indo-Gangetic Plains (IGP) is one of the largest and rapidly growing NH₃ hotspots of the world, with a growth rate of +1.2% yr^-1 in summer (June-August: Kharif season), due to intense agricultural activities and presence of many fertilizer industries there. However, our analyses show insignificant decreasing trends in annual NH₃ of about -0.8% yr^-1 in all India, about -0.4% yr^-1 in IGP, and -1.0% yr^-1 in the rest of India. Ammonia is positively correlated with total fertilizer consumption (r = 0.75) and temperature (r = 0.5) since high temperature favors volatilization, and is anti-correlated with total precipitation (r = from -0.2, but -0.8 in the Rabi season: October-February) as wet deposition helps removal of atmospheric NH₃. This study, henceforth, suggests the need for better fertilization practices and viable strategies to curb emissions, to alleviate the adverse health effects and negative impacts on the ecosystem in the region. On the other hand, the overall decreasing trend in atmospheric NH₃ over India shows the positive actions, and commitment to the national missions and action plans to reduce atmospheric pollution and changes in climate.

Validation of PM10 and PM2.5 early alert in Bogotá, Colombia, through the modeling software WRF-CHEM

Air quality data from Bogotá, Colombia, show high levels of particulate matter (PM), which often generate respiratory problems to the population and a high economic cost to the government. Since 2016, air quality in the city of Bogotá has been measured through the Bogota Air Quality Index (IBOCA) which works as an indicator of environmental risk due to air pollution. However, available technological tools in Bogotá are not enough to generate early alerts due to PM₁₀ and PM_2.5. Currently, alerts are only announced once the measured PM values exceed a certain standard (e.g., 37 μ g/m³), but not with enough anticipation to efficiently protect the population. It is necessary to develop an early air quality alert in Bogotá, in order to provide information that improves risk management protocols in the capital district. The purpose of this investigation is to validate the corrective alert presented on the 14th and 15th of February of 2019, through the WRF-Chem model under different weather conditions, using three different setups of the model to simulate PM₁₀ and PM_2.5 concentrations during two different climatic seasons and different resolutions. The results of this article generate a validation of two configurations of the model that can be used for the Environmental Secretary of the District (SDA) forecasts in Bogotá, Colombia, in order to contribute to the prediction of pollution events produced by PM₁₀ and PM_2.5 as a tool for an early alert system (EAS) at least 24 h in advance.

Deep learning for identifying environmental risk factors of acute respiratory diseases in Beijing, China: implications for population with different age and gender

This study focuses on identifying environmental health risk factors related to acute respiratory diseases using deep learning method. Based on respiratory disease data, air pollution data and meteorological environmental data, cross-domain risk factors of acute respiratory diseases were identified in Beijing, China. We conducted age and gender stratified deep neural network models in air pollution epidemiology. We ranked risk factors of respiratory diseases in stratified populations and conducted quantitative comparison. People ≥50 years were more sensitive to PM_2.5 pollution than <50 years people, especially women ≥50 years. Compared with women, both men ≥50 years and <50 years were more susceptible to PM₁₀. Young women <50 years were more sensitive to general air pollutants such as SO₂ and NO₂ than <50 years young men. Meteorological factors such as wind speed and precipitation could promote the diffusion of fine particulate matter and general air pollutants (SO₂, NO₂, etc.), which could help to reduce the incidence of acute respiratory diseases. This study represents a quantitative analysis of environmental health risk factors identification related to acute respiratory diseases based on deep neural network method. The results of this study could help people to improve their awareness of acute respiratory diseases prevention.

Exposure to organic and inorganic traffic-related air pollutants alters haematological and biochemical indices in albino mice Mus musculus

The relationship between air pollution exposure and haematology remains controversial. Evidences in the effect of trace organic air pollutants and in the impact of such exposure on lipid and protein levels are scarce. This work investigated the health effects of medium-term exposure to traffic-related air pollution on both haematological and biochemical indices in animal models. Two groups of albino mice (Mus musculus) were exposed to ambient air polluted by vehicle exhaust for three and six months, and one group was kept as control. Results found significant depletions (p < 0.05) in red blood cells, packed cell volume, neutrophils, eosinophils, monocytes, and total cholesterol after air pollution exposure. On the contrary, significant elevations (p < 0.05) were observed in platelet, lymphocytes, and serum albumin compared to control condition. Correlation data suggested that significant changes in blood parameters may be altered by the synergistic effect of several organic and inorganic air pollutants.

The burden of cardio-cerebrovascular disease and lung cancer attributable to PM2.5 for 2009, Guangzhou: a retrospective population-based study

Particulate matter pollution has become a widely-concerned issue in public health and led to a substantial loss of health. The study reports relationship between particulate matter with aerodynamic diameter <2.5 µm (PM_2.5) and years of life lost (YLL) in Guangzhou. A retrospective burden analysis on annual mean PM_2.5 data was conducted. Data on annual mortality were collected for 2009, from the Health Department of Guangzhou. Data on particulate matter were collected for period 2006-2009. Comparative risk assessment and exposure-response function were used to estimate attributable YLL. The exposure to PM_2.5 was associated with a total of 454.6 YLLs (95% uncertainty interval 449.0-460.1) per 100,000 people in 2009. This study has confirmed the substantial adverse health effects of PM_2.5 exposure in population with cardio-cerebrovascular disease and lung cancer. This study highlights the need to reduce ambient particulate pollution for better environmental health and lower burden of disease.

A review of the possible associations between ambient PM2.5 exposures and the development of Alzheimer's disease

PM2.5 particles in air pollution have been widely considered associated with respiratory and cardiovascular diseases. Recent studies have shown that PM2.5 can also cause central nervous system (CNS) diseases, including a variety of neurodegenerative diseases, such as Alzheimer's disease (AD). Activation of microglia in the central nervous system can lead to inflammatory and neurological damage. PM2.5 will reduce the methylation level of DNA and affect epigenetics. PM2.5 enters the human body through a variety of pathways to have pathological effects on CNS. For example, PM2.5 can destroy the integrity of the blood-brain barrier (BBB), so peripheral systemic inflammation easily crosses BBB and reaches CNS. The olfactory nerve is another way for PM2.5 particles to enter the brain. Surprisingly, PM2.5 can also enter the gastrointestinal tract, causing imbalances in the intestinal microecology to affect central nervous system diseases. The current work collected and discuss the mechanisms of PM2.5-induced CNS damage and PM2.5-induced neurodegenerative diseases.

Particulate air pollution: major research methods and applications in animal models

Ambient air pollution is composed of a heterogeneous mixture of gaseous and solid particle compounds in which primary particles are emitted directly into the atmosphere, such as diesel soot, while secondary particles are created through physicochemical transformation. Particulate matter (PM), especially fine and ultrafine particles, can be inhaled and deposited in the alveolar cavities and penetrate into circulation. An association between high levels of air pollutants and human disease has been known for more than half a century and increasing evidences demonstrate a strong link between exposure on PM and the development of systemic diseases, such as cardiovascular and neurological disorders. Experimental animal models have been extensively used to study the underlying mechanism caused by environmental exposure to ambient PM. Due to their availability, quality, cost, and genetically modified strains, rodent models have been widely used. Some common exposure approaches include intranasal instillation, intratracheal instillation, nose-only inhalation, whole-body inhalation, and intravenous injection have been reviewed with brief summary of its performance, merit, limitation, and application. We hope this would provide useful reference in advancing experimental researches about air pollution human health and disease development.