Influence of agricultural activities on atmospheric pollution during post-monsoon harvesting seasons at a rural location of Indo-Gangetic Plain

Global estimates of ambient NO2 concentrations and long-term health effects during 2000-2019

High concentrations of ambient NO2 causes serious air pollution and could also pose great threats to human health. However, the long-term trends (20-year) and potential health effects of ambient NO2 exposure globally still shows high uncertainties. In this work, the field measurements, satellite dataset, GEOS-Chem output, and multiple geographical covariates were incorporated into the multi-stage model to investigate the global evolutions of ambient NO2 during 2000-2019. The results indicated that the cross-validation (CV) R2 values of ambient NO2 based on multi-stage model displayed satisfied performance (R2 = 0.78), which was superior to the individual model. Besides, the out-of-bag R2 was 0.75, which suggested the multi-stage model showed the better transferability. At the spatial scale, the NO2 concentrations followed the order of China (16.9 ± 9.0 μg/m3) > India (15.5 ± 5.6 μg/m3) > United States (10.7 ± 5.6 μg/m3) > Europe (7.7 ± 4.5 μg/m3), which was in consistent with the anthropogenic NOx emission. At the temporal scale, the ambient NO2 levels in China experienced persistent increases (0.29 μg/m3/year) during 2000-2013, whereas they showed slight decreases (-0.23 μg/m3/year) during 2013-2019. The ambient NO2 levels in the United States experienced continuous decreases during 2000-2019 (-0.20 μg/m3/year), while both of India and Europe remained relatively stable. Long-term NO2 exposure inevitably increased premature mortalities. The global premature all-cause mortalities associated with the excessive NO2 exposure increased from 288,169 (95% CI: 43,650, 527,971) to 461,301 (95% CI: 69,973, 843,996) in the past 20 years. This study would provide sufficient policy support for future ambient NO2 mitigation.

In vitro inhalation bioaccessibility and health risk assessment of difenoconazole in the atmosphere

BACKGROUND

Assessment of the risk of pesticide inhalation in populations around farmland is necessary because inhalation is one of the ways in which pesticides can risk human health. This study aimed to identify the inhalation risk of difenoconazole on humans by using dose-response and exposure assessments.

RESULTS

In the field simulation application, respiratory exposure in populations around farmland ranged from 71 to 430 ng/m3 . Using response surface methodology, the maximum bioaccessibility of difenoconazole in three simulated lung fluids was 35.33% in Gamble's solution (GS), 34.12% in artificial lysosomal fluid (ALF), and 42.06% in simulated interstitial lung fluid (SLF). Taking the proliferation activity of the A549 cell model as the endpoint, the benchmark dose limit and benchmark dose of difenoconazole on A549 cells were 16.36 and 5.60 mg/kg, respectively. The margin of exposure to difenoconazole in GS, ALF and SLF were, respectively, 8.66 × 105 to 5.28 × 106 , 8.97 × 105 to 5.47 × 106 and 7.28 × 105 to 4.44 × 106 .

CONCLUSION

The risk assessment results indicate that under all circumstances, applying difenoconazole is safe for populations around farmland. However, a fan-shaped nozzle, suspension concentrate and greater inhalation height increase the risk of inhalation. © 2023 Society of Chemical Industry.

Agricultural abandoned lands as emission sources of dust containing metals and pesticides in the Sonora-Arizona Desert

This investigation examines the transport of metal- and pesticide-polluted dust emitted by one of the most relevant agricultural areas of Northwestern Mexico. In the contaminated area, an excessive water extraction of the aquifer and seawater intrusion caused the abandonment of fields, which are pollutant-loaded dust emitters. We used air mass forward trajectories (HYSPLIT) model to obtain particle trajectories in the wind and the use of banned pesticides as geochemical tracers for dust transported by wind. Fifty dust samples from 10 agriculture fields and 26 roof dust of a city close to the agricultural area were analyzed for their contents of zirconium, lead, arsenic, zinc, copper, iron, manganese, vanadium, and titanium, by portable X-ray fluorescence. Nine pesticides were analyzed in the roof dust and agricultural soil samples by gas chromatography. Results show that the distribution of metals was significantly different between active and abandoned fields. Arsenic-lead-copper was mainly concentrated in abandoned fields, while zinc-iron-manganese-titanium was dominant in active fields. Two potential sources of metal contamination were found by principal component analysis (PCA): (I) a mixture of traffic and agricultural sources and (II) a group related to agricultural activities. The occurrence of banned pesticides in dust deposited on roofs collected at nearby cities confirms the atmospheric transport from the agricultural area. The HYSPLIT results indicated that the dust emitted from agricultural fields can reach up to the neighboring states of Sonora, Mexico, and the USA. The impacts that these emissions can have on human health should be studied in future research.

Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi

Stubble-burning in northern India is an important source of atmospheric particulate matter (PM) and trace gases, which significantly impact local and regional climate, in addition to causing severe health risks. Scientific research on assessing the impact of these burnings on the air quality over Delhi is still relatively sparse. The present study analyzes the satellite-retrieved stubble-burning activities in the year 2021, using the MODIS active fire count data for Punjab and Haryana, and assesses the contribution of CO and PM_2.5 from such biomass-burning activities to the pollution load in Delhi. The analysis suggests that the satellite-retrieved fire counts in Punjab and Haryana were the highest among the last five years (2016-2021). Further, we note that the stubble-burning fires in the year 2021 are delayed by ∼1 week compared to that in the year 2016. To quantify the contribution of the fires to the air pollution in Delhi, we use tagged tracers for CO and PM_2.5 emissions from fire emissions in the regional air quality forecasting system. The modeling framework suggests a maximum daily mean contribution of the stubble-burning fires to the air pollution in Delhi in the months of October-November 2021 to be around 30-35%. We find that the contribution from stubble burning activities to the air quality in Delhi is maximum (minimum) during the turbulent hours of late morning to afternoon (calmer hours of evening to early morning). The quantification of this contribution is critical from the crop-residue and air-quality management perspective for policymakers in the source and the receptors regions, respectively.

Estimation of agricultural burned affected area using NDVI and dNBR satellite-based empirical models

One of the major crucial issues that need worldwide attention is open stubble burning, which imposes a variety of adverse impacts on nature and human society, destroying the world's biodiversity. Many earth observation satellites render information to monitor and assess agricultural burning activities. In this study, different remotely sensed data (Sentinel-2A, VIIRS) has been employed to estimate the quantitative measurements of agricultural burned areas of the Purba Bardhaman district from October-December 2018. The multi-temporal image differencing techniques and indices (NDVI, NBR, and dNBR) and VIIRS active fires data (VNP14IMGT) have been utilized to spot agricultural burned areas. In the case of the NDVI technique, a prominent area, 184.82 km² of agricultural burned area (7.85% of the total agriculture), was observed. The highest (23.04 km²) burned area was observed in the Bhatar block, located in the middle part of the district, and the lowest (0.11 km²) burned area was observed in the Purbasthali-II block, which is located in the eastern part of the district. On the other hand, the dNBR technique revealed that the agricultural burned areas enwrap 8.18% of the total agricultural area, which is 192.45 km². As per the earlier NDVI technique, the highest agricultural burned areas (24.82 km²) were observed in the Bhatar block, and the lowest (0.13 km²) burn area occurred in the Purbashthali-II block. In both cases, it is observed that agricultural residue burning is high in the western part of the Satgachia block and the adjacent areas of the Bhatar block, which is in the middle part of Purba Bardhaman. The agricultural burned area was extracted using different spectral separability analyses, and the performance of dNBR was the most effective in spectral discrimination of burned and unburned surfaces. This study manifested that agricultural residue burning started in the central part of Purba Bardhaman. Later it spread all over the district due to the trend of early harvesting rice crops in this region. The performance of different indices for mapping the burned areas was evaluated and compared, revealing a strong correlation (R2) = 0.98. To estimate the campaign's effectiveness against the dangerous practice and plan the control of the menace, regular monitoring of crop stubble burning using satellite data is required.

Understanding the influence of summer biomass burning on air quality in North India: Eight cities field campaign study

Near real-time monitoring of major air pollutants, i.e., particulate matter (PM₁₀, PM_2.5, PM₁), trace gases (O₃, CO, NO, NO₂, NO_x, NH₃, CO₂, SO₂) and Volatile Organic Compounds (VOCs: benzene, ethylbenzene, m-, p-xylene, o-xylene and toluene) along with climatological parameters was done in eight-cities field campaigns during the rabi (wheat) crop residue burning period in the northwest of Indo-Gangetic Plain (IGP) region. The phase-wise monitoring was done at eight locations representing rural, semi-urban and urban backgrounds. During the whole campaign, the semi-urban site (Sirsa) observed the highest average concentration of PM₁₀ (226 ± 111 μg m^-3) and PM_2.5 (91 ± 67 μg m^-3). The urban site (Chandigarh) reported the minimum concentrations of all the three size fractions of particulate matter with PM₁₀ as 89 ± 54 μg m^-3, PM_2.5 as 42 ± 22 μg m^-3 and PM₁ as 20 ± 13 μg m^-3 where the monitoring was done in the early phase of the campaign. The highest VOC concentration was recorded at the semi-urban (Sirsa) site, whereas the lowest was at a rural location (Fatehgarh Sahib). NH₃ concentration was observed highest in rural sites (31.7 ± 29.8 ppbv), which can be due to the application of fertilizers in agricultural activities. Visible Infrared Imaging Radiometer Suite (VIIRS) based fire and thermal anomalies, along with HYSPLIT back trajectory analysis, show that major air masses over monitoring sites (22 %-70 %) were from the rabi crop residue burning regions. The characteristic ratios and Principal component analysis (PCA) results show that diverse sources, i.e., emissions from crop residue burning, solid biomass fuels, vehicles and industries, majorly degrade the regional air quality. This multi-city study observed that semi-urban regions have the most compromised air quality during the rabi crop residue burning and need attention to address the air quality issues in the IGP region.

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.

Preventable mortality attributable to exposure to air pollution at the rural district of Punjab, India

Air pollution has emerged as a leading global health risk in recent decades, where its health impacts are primarily focused on urban areas. In India, semi-urban and rural areas are also emerging as air pollution hotspots. As these regions are out of focus, the current study monitored air pollution (PM_2.5) at a rural district of Punjab in North India and attempted to study the associated health impacts. Hospital data of outpatient department (OPD), inpatient department (IPD) and all-cause mortality was correlated with change in PM_2.5 concentrations. PM_2.5 concentrations showed seasonal variations having relatively higher concentrations during post-monsoon and winter seasons. This rise in air pollution (annual average 92 µg/m³) was found to be mainly due to crop residue burning, including local meteorology. In comparison, hospital data shows that hospital visits and admissions were higher during monsoon. This shows that hospital admissions could not be directly linked to air pollution in rural areas as other factors such as short days during winters and self-medication, socio-economic factors and dependency on local unauthorised traditional healers may influence. The application of the AirQ + model for short-term health effects reveals that out of 2582 total deaths, preventable deaths ranging from 246 (WHO guidelines value - 10 µg/m³) to 159 (Indian NAAQS - 40 µg/m³) could be ascribed to air pollution exposure and specifically PM_2.5. However, these deaths are avoidable by developing strategies to minimise air pollution in rural areas. Hence, a comprehensive approach is needed to plan air pollution reduction strategies, including urban, semi-urban and rural areas.

Seasonal variations in carbonaceous species of PM2.5 aerosols at an urban location situated in Indo-Gangetic Plain and its relationship with transport pathways, including the potential sources

The study examines the variation in organic carbon (OC) and elemental carbon (EC) in PM_2.5 concentration at an urban location of Indo-Gangetic Plains (IGP) to understand the impact of seasonality and regional crop residue burning activities. Seasonal cluster analysis of backward air masses and concentration-weighted trajectory (CWT) analysis was performed to identify seasonal transport pathways and potential source regions of carbonaceous aerosols. The mean PM_2.5 level during the study period was 57 ± 41.6 μgm^-3 (5.0-187.3 μgm^-3), whereas OC and EC concentration ranges from 2.8 μgm^-3 to 28.2 μgm^-3 and 1.3 μgm^-3 to 15.5 μgm^-3 with a mean value of 8.4 ± 5.5 μgm^-3 and 5.1 ± 3.3 μgm^-3 respectively. The highest mean PM_2.5 concentration was found during the winter season (111.3 ± 25.5 μgm^-3), which rises 3.6 times compared to the monsoon season. OC and EC also follow a similar trend having the highest levels in winter. Total carbonaceous aerosols contribute ∼38% of PM_2.5 composition. The positive linear trend between OC and EC identified the key sources. HYSPLIT cluster analysis of backward air mass trajectories revealed that during the post-monsoon, winters, pre-monsoon, and monsoon, 71%, 81%, 60%, and 43% of air masses originate within the 500 km radius of IGP. CWT analysis and abundance of OC in post-monsoon and winters season establish a linkage between regional solid-biomass fuel use and crop residue burning activities, including meteorology. Moreover, the low annual average OC/EC ratio (1.75) indicates the overall influence of vehicular emissions. The current dataset of carbonaceous aerosols collated with other Indian studies could be used to validate the global aerosol models on a regional scale and aid in evidence-based air pollution reduction strategies.

COVID-19 pandemic and sudden rise in crop residue burning in India: issues and prospects for sustainable crop residue management

The seasonal burning of crop residue significantly affects the environment, leading to poor air quality over Indo-Gangetic Plain (IGP) in India. Hence, there have been significant efforts to minimize crop residue burning through policy, innovations, and awareness measures. However, an abrupt increase in paddy residue burning was observed over IGP during 2020. Hence, the study explores the factors leading to this sharp rise. The business as usual trends analysis revealed that paddy crop residue burning activities increased significantly (60%) in 2020 compared to the previous year. The massive increase in crop residue burning consequently seems to be linked with the COVID-19 pandemic, which affected the farmer's income, including the poor compliance by the regulatory authorities. The study also highlights the issues and prospects for sustainable crop residue management and explores the solutions to minimize crop residue burning. There are few crops in India that have guaranteed minimum sale price and are also subsidized. These provisions encourage farmers to grow those particular crops, resulting in the generation of large amounts of crop residue from these specific crops. There have been several efforts by the Indian government, including based on recent court intervention. Still, there is no respite from burning activities and the occurrence of Delhi winter smog every year. Hence, the study emphasizes a need to adopt integrated approaches having in situ eco-friendly solutions, which enhances the farmer's income and focuses on employability, capacity building, awareness generation, and in situ economically viable solutions.

Understanding seasonal variation in ambient air quality and its relationship with crop residue burning activities in an agrarian state of India

In India, Indo-Gangetic Plains (IGP) is becoming the hotspot of air pollution due to increasing anthropogenic activities such as rapid industrial growth, infrastructure development, transportation activities, and seasonal practice of crop residue burning. In the current study, seasonal variation in ambient air quality for 14 parameters, i.e., particulate matter (PM), trace gases, and volatile organic compounds (VOCs), along with meteorological parameters, was studied in 21 districts of the Haryana state for year 2019, situated in IGP. To analyze spatial variation of pollutants, ambient air quality data of 23 continuous ambient air quality monitoring stations were divided into three zones based on ecology and cropping pattern. All the zones showed annual mean PM₁₀ and PM_2.5 concentrations much higher than national ambient air quality standards. Annual mean PM₁₀ concentration (±standard deviation) in Zones-1, 2, and 3 was 156±86, 174±93, and 143±74 μg m^-3, whereas for PM_2.5 was 71±44, 85±54, and 78±47 μg m^-3. The results showed a considerable seasonal variation in the concentration of all pollutants. Most of the pollutants peak in the post-monsoon season, followed by winters in which crop residue burning predominates in many parts of the Haryana. PM₁₀ concentrations increased by 65-112% and PM_2.5 concentrations increased by 131-147% in the post-monsoon season compared to monsoons. The post-monsoon season showed the highest concentration of PM₁₀, NO, and toluene (Zone-1); and PM_2.5, NH₃, CO, and benzene (Zone-2); whereas in winters, SO₂ (Zone-1); ethylbenzene, m,p-xylene, and xylene (Zone-2); and NO₂ and NOx (Zone-3) showed the maximum pollution levels. The O₃ concentration was highest in the pre-monsoon season (Zone-1). The satellite-based fire counts and PCA results show a significant influence of crop residue burning in the post-monsoon season and solid biomass burning in winters on Haryana's air quality. The study could help to understand seasonal variation in ambient air quality and the influence of factors such as crop residue burning in the IGP region, which could help to formulate season-specific control measures to improve regional air quality.