Influence of Vehicular Emissions (NO, NO2, CO and NMHCs) on the Mixing Ratio of Atmospheric Ammonia (NH3) in Delhi, India

Spatial Distributions of Atmospheric Ammonia in a Rural Area in South Korea and the Associated Impact on a Nearby Urban Area

Ammonia (NH3) plays an important role in air quality and atmospheric chemistry, yet studies on the characteristics and impacts of NH3 are limited. Herein, we revealed the spatial distribution of atmospheric NH3, as measured by passive samplers, at three different sites (R1, R2, and R3) in the rural area (livestock environment) of Jeongeup, South Korea, from September 2019 to August 2020. At site R1, the boundary of a large-scale pig farm, dramatically high daily mean concentrations of NH3 were observed (118.7 ppb), whereas sites R2 and R3, located ~1 km from R1, exhibited lower concentrations of 18.2 and 30.4 ppb, respectively. In the rural environment, the monthly NH3 variations showed a peak in June (34.2 ppb), which was significantly higher than in the urban and remote areas. To examine the impact of NH3 from the rural area on a nearby urban area in June 2020, simultaneous measurements were performed using a real-time instrument in Jeonju. When high NH3 events occurred in the urban area in June, the results for the NH3 concentrations and observed meteorological conditions in the rural and urban areas showed that the rural area influenced the NH3 levels in the adjacent urban area.

Immunosuppression, oxidative stress, and apoptosis in pig kidney caused by ammonia: Application of transcriptome analysis in risk assessment of ammonia exposure

Ammonia (NH₃) is a recognized environmental contaminant around the world and has adverse effects on animal and human health. However, the mechanism of the renal toxicity of NH₃ is not well understood. Pigs are considered an ideal model for biomedical and toxicological research because of the similarity to humans in physiological and biochemical basis. Therefore, in this study, twelve pigs were selected as research objects and randomly divided into two groups, namely the control group and the NH₃ group. The formal experiment lasted 30 days. The effects of excessive NH₃ inhalation on the kidney of fattening pig were evaluated by chemical analysis, ELISA, transcriptome analysis and real-time quantitative PCR (qRT-PCR) from the renal antioxidant level, renal function, blood ammonia content and gene level. Our results showed that excessive NH₃ exposure could cause an increase in blood NH₃ content, a reduction in renal GSH-Px, SOD and GSH, as well as an increase in MDA levels and an increase in serum creatinine, urea and uric acid levels. In addition, transcriptome analysis showed that NH₃ exposure caused changes in 335 differentially expressed genes (DEGs) (including 126 up-regulated DEGs and 109 down-regulated DEGs). Some highly expressed DEGs were enriched into GO terms associated with immune function, oxidative stress, and apoptosis and were verified by qRT-PCR. The qRT-PCR results were comsistent with the transcriptome results. Our results indicated that NH₃ exposure could cause changes in renal transcriptional profiles and kidney function, and induce kidney damage in the fattening pigs through oxidative stress, immune dysfunction and apoptosis. Our present study provides novel insights into the immunotoxicity mechanism of NH₃ on kidney.

Investigating the seasonal variability in source contribution to PM2.5 and PM10 using different receptor models during 2013-2016 in Delhi, India

The present work deals with the seasonal variations in the contribution of sources to PM_2.5 and PM₁₀ in Delhi, India. Samples of PM_2.5 and PM₁₀ were collected from January 2013 to December 2016 at an urban site of Delhi, India, and analyzed to evaluate their chemical components [organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements]. The average concentrations of PM_2.5 and PM₁₀ were 131 ± 79 μg m^-3 and 238 ± 106 μg m^-3, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM_2.5 and PM₁₀ were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM_2.5 and PM₁₀ as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM_2.5 and PM₁₀. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.

Air quality index and criteria pollutants in ambient atmosphere over selected sites:Impact and lessons to learn from COVID-19

World Health Organization (WHO) declared the disease COVID-19, caused by novel virus SARS-CoV-2, as pandemic in March 2020 following which different countries adopted immediate stringent measures to save human lives. In order to restrict the spread of disease, intra- and interstate movements of public by road/rail and air were completely halted except for those involved in essential services. India followed the option of complete lockdown (LD) in phase 1 (25th March to 14th April 2020) followed by phase 2 (15th April to 3rd May), phase 3 (4th May to 17th May), and phase 4 (18th May to 31st May 2020). Restrictions on agriculture and industrial sector were eased out in each successive phase after LD 1 and eventually country started unlock phase from 1st June to 30th June and so on. In this chapter, a discussion is made based on the data on criteria pollutants and air quality index (AQI) for selected sites in New Delhi and for selected cities across the country, respectively to spell out impacts of COVID-19 (which are initially positive) and the lessons for long-term planning for improvement of air quality. During LD 1, emissions from vehicles, industries, and construction activities were substantially down and therefore, levels of criteria pollutants CO, NOx, SO₂, Pb, O₃, PM_2.5 in ambient atmosphere came down and AQI showed improvement which was considered as silver line of the COVID-19 pandemic. Interestingly, not all criteria pollutants showed decrease to same extent and rather some still stayed above the National Ambient Air Quality Standard (NAAQS) values over New Delhi, the national capital of India. As the restrictions eased out in LD 3 and LD 4, pollution levels started increasing but again not uniformly for all pollutants and for all locations in New Delhi. During Unlock 1.0, emissions did not increase suddenly rather in a systematic linear fashion. In addition, air quality and AQI did not deteriorate much during Unlock 1.0 due to onset of monsoon rains in the country. It is suggested that all emission sources and people’s movement cannot be restricted during normal days in any country, as it has hit the socioeconomic health of nation very hard. A close examination of pollution levels and emission sources during lockdown to unlock could help us in devising a long-term strategy to bring down air pollution particularly in worst hit regions such as New Delhi. Systematic restrictions on major pollution sources with intermittent relaxations could be a possible approach to constrain air pollution.