Variation Of NO Research Articles

Spatio-Temporal Analysis of Air Pollution in Sharjah City Pre-to-Post COVID-19 Lockdown

This study presents an analysis of O3, NO2, and PM10 variations in Sharjah City, UAE, from 2018 to 2021. Data from monitoring stations across the city showed consistent daily, weekly, and monthly trends. Ozone and NO2 were inversely related, with O3 increasing and NO2 decreasing from noon to sunset, and vice versa afterward. Weekends exhibited a weekend effect, with high O3 and low NO2 levels. Winters had high NO2 and low O3 levels, while summers showed the opposite trend. Urban and industrial stations had higher NO2 and lower O3 levels compared to sub-urban and rural stations. PM10 levels peaked in the afternoon to late evenings and decreased during nights and early mornings. They were lowest on weekends and highest at the start of the weeks, with July as the peak and December as the lowest month for PM10. During the COVID-19 restrictions of 2020-2021, O3, NO2, and PM10 levels significantly decreased compared to 2018-2019. Higher winds correlated with higher O3 and PM10 levels and lower NO2 levels. This study provides the first documentation of Sharjah’s air quality spatiotemporal variations pre-to-post COVID-19 restrictions using ground measurements.

Long-term spatiotemporal changes in nitrate contamination of municipal groundwater resources after sewerage network construction in the Hungarian Great Plain.

Over the last decades, as a consequence of wastewater discharges and other anthropogenic sources, severe nitrate (NO3-) pollution has developed in municipal environment causing global concern. Thus, eliminating the potential sources of pollution is one of the major challenges of the twenty-first century, whereby sanitation services are essential for ensuring public health and environmental protection. In the present study, long-term monitoring (2011-2022) of shallow groundwater NO3- contamination in municipal environment was carried following the construction of the sewerage network (2014) in the light of the pre-sewerage situation. Our primary aim was to assess the long-term effects of sewerage on nitrate NO3- levels in the shallow groundwater and evaluate the efficiency of these sanitation measures over time. Based on the results, significant pollution of the shallow groundwater in the municipality was identified. During the pre-sewer period, NO3- concentrations exceeded the 50mg/L limit in the majority of monitoring wells significantly, upper quartile values ranged between 341 and 623mg/L respectively. Using Nitrate Pollution Index (NPI) and interpolated NO3- pollution maps, marked spatial north-south differences were detected. In order to verify the presence of wastewater discharges in the monitoring wells, the isotopic ratio shifts (δ) for 18O and D(2H) were determined, confirming municipal wastewater effluent. Variations in NO3-/Cl- molar ratios suggest also contamination from anthropogenic sources, including septic tank effluent from households and the extensive use of manure. Data series of 7years (2015-2022) after the investment indicate marked positive changes by the appearance of decreasing trends in NO3- values confirmed by Wilcoxon signed rank test and ANOVA. By comparing the pre- and post-sewerage conditions, the mean NO3- value decreased from 289.7 to 175.6mg/L, with an increasing number of monitoring wells with concentrations below the limit. Our results emphasise the critical role of sanitation investments, while also indicating that the decontamination processes occur at a notably slow pace. Detailed, long-term monitoring is therefore essential to ensure accurate follow-up of the ongoing changes. The results can provide information for local citizens and authorities to improve groundwater management tools in the region.

Variations in air pollution before, during and after the COVID-19 lockdown in Peruvian cities.

The high concentrations of air pollutants in Peru remain a persistent problem, significantly impacting public health. Understanding the extent to which the COVID-19 lockdown affected these contaminants is crucial. To determine variations in NO2, O3, CO, and SO2 concentrations in 10 Peruvian cities before, during, and after lockdown. A comparative ecological study was conducted in urban areas of 10 major Peruvian cities using the Google Earth Engine (GEE) platform. Data on atmospheric pollutant concentrations were extracted from the Sentinel-5P/TROPOMI satellite images for the period between March 16 and June 30, across the years 2019, 2020, 2021, and 2022, for comparative analysis. The Wilcoxon test was used to evaluate changes between the study periods. We included 10 urban cities located across three geographic regions of Peru. Most urban cities experienced a decrease in NO2 concentrations and an increase in O3 and CO levels during the lockdown, while SO2 concentrations remained relatively constant. The lockdown has caused variations in NO2, O3 and CO concentrations. Future studies with accurate data on air pollutant concentrations are needed to ensure targeted and effective interventions.

Validation of GEMS tropospheric NO2 columns and their diurnal variation with ground-based DOAS measurements

Abstract. Instruments for air quality observations on geostationary satellites provide multiple observations per day and allow for the analysis of the diurnal variation in important air pollutants such as nitrogen dioxide (NO2). The South Korean instrument GEMS (Geostationary Environmental Monitoring Spectrometer), launched in February 2020, is the first geostationary instrument that is able to observe the diurnal variation in NO2. The measurements have a spatial resolution of 3.5 km × 8 km and cover a large part of Asia. This study compares 1 year of tropospheric NO2 vertical column density (VCD) observations from the operational GEMS L2 product, the scientific GEMS IUP-UB (Institute of Environmental Physics at the University of Bremen) product, the operational TROPOspheric Monitoring Instrument (TROPOMI) product, and ground-based differential optical absorption spectroscopy (DOAS) measurements in South Korea. The GEMS L2 tropospheric NO2 VCDs overestimate the ground-based tropospheric NO2 VCDs with a median relative difference of +61 % and a correlation coefficient of 0.76. The median relative difference is −2 % for the GEMS IUP-UB product and −16 % for the TROPOMI product, with correlation coefficients of 0.83 and 0.89, respectively. The scatter in the GEMS products can be reduced when observations are limited to the TROPOMI overpass time. Diurnal variations in tropospheric NO2 VCDs differ by the pollution level of the analyzed site but with good agreement between the GEMS IUP-UB and ground-based observations. Low-pollution sites show weak or almost no diurnal variation. In summer, the polluted sites show a minimum around noon, indicating the large influence of photochemical loss. Most variation is seen in spring and autumn, with increasing NO2 in the morning, a maximum close to noon, and a decrease towards the afternoon. Winter observations show rather flat or slightly decreasing NO2 throughout the day. Winter observations under low-wind-speed conditions at high-pollution sites show enhancements of NO2 throughout the day. This indicates that under calm conditions, dilution and the less effective chemical loss in winter do not balance the accumulating emissions. Diurnal variation observed at a low-pollution site follows seasonal wind patterns. A weekday–weekend effect analysis shows good agreement between the different products. However, the GEMS L2 product, while agreeing with the other data sets on weekdays, shows significantly less reduction on weekends. The influence of the stratospheric contribution and the surface reflectivity product on the satellite tropospheric NO2 VCD products is investigated. While the TM5 model's stratospheric VCDs, used in the TROPOMI product, are too high, resulting in tropospheric NO2 VCDs that are too low and even negative, when used in the GEMS IUP-UB retrieval, the GEMS L2 stratospheric VCD is too low. Surface reflectivity comparisons indicate that the GEMS L2 reflectivity makes a large contribution to the observed overestimation and scatter.

Response of CH4 and N2O emissions to the feeding rates in a pond rice-fish co-culture system.

Feeding rate is an important factor influencing the carbon and nitrogen input and greenhouse gas emission from aquaculture systems. However, the quantitative relationship between feeding rates and GHG emissions is still poorly understood. In this study, we conducted a laboratory-scale experiment to examine the impact of feeding rate (0%, 2%, 4%, 6%, and 8%) on the CH4 and N2O emissions from a pond rice-fish co-culture system. The results showed that the total amount of CH4 emission did not significantly differ when the feeding rate was no more than 6%, but increased more than four times when the feeding rate reach to 8%. The amount of N2O emission showed a linearly increasing trend with the feeding rate. The emission factors of CH4 and N2O was significantly higher for 8% feeding rate than other feeding rates. The variation of CH4 emission was primarily attributed to the ratio of mcrA/pmoA in the sediment and the contents of biological oxygen demand (COD) and dissolved oxygen (DO) in the water; and the variation of N2O was primarily affected by the available nitrogen in the water and sediment and the content of DO in the water. The overall emission of CH4 and N2O showed an exponential relationship with feeding rate. The total yields of fish and rice did not continuously increase when the feeding rate exceeded 4%. The lowest emission intensity per unit yield was reached at the feeding rate of 2.99%. These results can provide a reference for the determination of low-carbon feeding strategy for pond rice-fish co-culture system.

Seasonal variation in indoor NO2 in urban residences in Mumbai and examining its determinants

Seasonal variation in indoor NO2 in urban residences in Mumbai and examining its determinants

Changes in source composition of wet nitrate deposition after air pollution control in a typical area of Southeast China

In recent years, China has adopted numerous policies and regulations to control NOx emissions to further alleviate the adverse impacts of NO3−-N deposition. However, the variation in wet NO3−-N deposition under such policies is not clear. In this study, the southeastern area, with highly developed industries and traditional agriculture, was selected to explore the variation in NO3−-N deposition and its sources changes after such air pollution control through field observation and isotope tracing. Results showed that the annual mean concentrations of NO3−-N in precipitation were 0.67 mg L−1 and 0.54 mg L−1 in 2014–2015 and 2021–2022, respectively. The average wet NO3−-N depositions in 2014–2015 and 2021–2022 was 7.76 kg N ha−1 yr−1 and 5.03 kg N ha−1 yr−1, respectively, indicating a 35% decrease. The δ15N–NO3− and δ18O–NO3− values were lower in warm seasons and higher in cold seasons, and both showed a lower trend in 2021–2022 compared with 2014–2015. The Bayesian model results showed that the NOx emitted from coal-powered plants contributed 53.6% to wet NO3−-N deposition, followed by vehicle exhaust (22.9%), other sources (17.1%), and soil emissions (6.4%) during 2014–2015. However, the contribution of vehicle exhaust (33.3%) overpassed the coal combustion (32.3%) and followed by other sources (25.4%) and soil emissions (9.0%) in 2021–2022. Apart from the control of air pollution, meteorological factors such as temperature, precipitation, and solar radiation are closely related to the changes in atmospheric N transformation and deposition. The results suggest phased achievements in air pollution control and that more attention should be paid to the control of motor vehicle exhaust pollution in the future, at the same time maintaining current actions and supervision of coal-powered plants.

Deciphering and quantifying nitrate sources and processes in the central Yellow Sea using dual isotopes of nitrate

Anthropogenic activities pose significant challenges to the accumulation of coastal nitrogen (N). Accurate identification of nitrate (NO3−) sources is thus essential for mitigating excessive N in many marginal seas. We investigated the dual isotopes of NO3− in the central Yellow Sea to elucidate the sources and cycling processes of NO3−. The results revealed significant spatial variability in NO3− concentrations among the Yellow Sea Surface Water (YSSW), Changjiang Diluted Water (CDW), Yellow Sea Cold Water Mass (YSCWM), and Taiwan Warm Current Water (TWCW). Stratification played a crucial role in restricting vertical nutrient transport, leading to distinct nutrient sources and concentrations in different water masses. The dual NO3− isotopic signature indicated that atmospheric deposition was the primary source of surface NO3−, contributing approximately 30 % to the NO3− in the YSSW. In the NO3−-rich CDW, the heavier δ15N-NO3− and δ18O-NO3− suggested incomplete NO3− assimilation. Organic matter mineralization and water stratification played crucial roles in the accumulation of nutrients within the YSCWM and TWCW. Notably, regenerated NO3− accounted for approximately half of the NO3− stored in the YSCWM. A synthesis of NO3− dual isotope data across the coastal China seas revealed significant spatial and seasonal variations in the N source. The study emphasized the dynamics of coastal NO3− supply, which are shaped by the complex interconnections among marine, terrestrial, and atmospheric processes. Our approach is a feasible method for exploring the origins of N amidst the escalating pressures of anthropogenic nutrient pollution in coastal waters.

Interpreting Geostationary Environment Monitoring Spectrometer (GEMS) geostationary satellite observations of the diurnal variation in nitrogen dioxide (NO2) over East Asia

Abstract. Nitrogen oxide radicals (NOx≡NO+NO2) emitted by fuel combustion are important precursors of ozone and particulate matter pollution, and NO2 itself is harmful to public health. The Geostationary Environment Monitoring Spectrometer (GEMS), launched in space in 2020, now provides hourly daytime observations of NO2 columns over East Asia. This diurnal variation offers unique information on the emission and chemistry of NOx, but it needs to be carefully interpreted. Here we investigate the drivers of the diurnal variation in NO2 observed by GEMS during winter and summer over Beijing and Seoul. We place the GEMS observations in the context of ground-based column observations (Pandora instruments) and GEOS-Chem chemical transport model simulations. We find good agreement between the diurnal variations in NO2 columns in GEMS, Pandora, and GEOS-Chem, and we use GEOS-Chem to interpret these variations. NOx emissions are 4 times higher in the daytime than at night, driving an accumulation of NO2 over the course of the day, offset by losses from chemistry and transport (horizontal flux divergence). For the urban core, where the Pandora instruments are located, we find that NO2 in winter increases throughout the day due to high daytime emissions and increasing NO2/NOx ratio from entrainment of ozone, partly balanced by loss from transport and with a negligible role of chemistry. In summer, by contrast, chemical loss combined with transport drives a minimum in the NO2 column at 13:00–14:00 local time (LT). Segregation of the GEMS data by wind speed further demonstrates the effect of transport, with NO2 in winter accumulating throughout the day at low winds but flat at high winds. The effect of transport can be minimized in summer by spatially averaging observations over the broader metropolitan scale, under which conditions the diurnal variation in NO2 reflects a dynamic balance between emission and chemical loss.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.

Temporal characteristics and health effects related to NO2, O3, and SO2 in an urban area of Iran

This study reports on temporal variations of NO2, O3, and SO2 pollutants and their related health effects in urban air of Khorramabad, Iran using AirQ 2.2.3 software. Based on data between 2015 and 2021, hourly NO2, O3, and SO2 concentrations increase starting at 6:00 a.m. local time until 9:00 p.m., 3:00 p.m., and 7:00 p.m. local time, respectively, before gradually decreasing. The highest monthly NO2, O3, and SO2 concentrations are observed in October, August, and September, respectively. Annual median NO2, O3, and SO2 concentrations range between 17 ppb and 38.8 ppb, 17.5 ppb–36.6 ppb, and ∼14 ppb–30.8 ppb, respectively. Two to 93 days and 17–156 days between 2015 and 2021 exhibit daily concentrations of NO2 and SO2 ≤ WHO AQGs, respectively, while 187–294 days have 8-h maximum O3 concentrations ≤ WHO AQGs. The mean excess mortality ascribed to respiratory mortality, cardiovascular mortality, hospital admissions for COPD, and acute myocardial infraction are 121, 603, 39, and 145 during 2015–2021, respectively. O3 is found to exert more significant health effects compared to SO2 and NO2, resulting in higher cardiovascular mortality. The gradual increase in NO2 and possibly O3 over the study period is suspected to be due to economic sanctions, while SO2 decreased due to regulatory activity. Sustainable control strategies such as improving fuel quality, promoting public transportation and vehicle retirement, applying subsidies for purchase of electric vehicles, and application of European emission standards on automobiles can help decrease target pollutant levels in ambient air of cities in developing countries.

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018).

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

Association between NO2 and human mobility: a two-year spatiotemporal study during the COVID-19 pandemic in Southeast Asia

ABSTRACT In the wake of the COVID-19 pandemic, global efforts to mitigate the spread of the virus have led to widespread lockdowns and movement restrictions. Earlier studies have reported a notable positive correlation between nitrogen dioxide (NO2) levels and mobility during the initial 2020 lockdowns. However, our explorative investigation in Southeast Asia observed that, despite their similar spatial distribution, NO2 does not consistently align with mobility patterns. This observation indicates the existence of additional influential factors apart from mobility contributing to NO2 variation, necessitating a more comprehensive examination. Subsequently, we developed a trained Multi-Layer Perceptron (MLP) model and leveraged SHapley Additive exPlanations (SHAP) values. Our analysis extends beyond mobility to encompass diverse potential factors such as travel modes and meteorological factors. The model results suggest that, while as expected mobility has a strong impact on NO2 column density, a more accurate prediction requires considering different travel modes (i.e. driving and walking). Furthermore, the study reveals that spatio-temporal heterogeneity and meteorological factors also play roles in shaping NO2 level. These findings emphasize the importance of integrating such multifaceted considerations in future NO2 study for a more accurate and holistic understanding.

Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model

This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques—Boosted Regression Trees (BRTs) and Random Forest (RF) models—we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2–9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.

Exploring data quality and seasonal variations of N2O in wastewater treatment: a modeling perspective

Abstract In this work, operational data collected from four Danish wastewater treatment plants (WWTP) are assessed for quality issues and analyzed to investigate the feasibility of data-driven modeling for control purposes. All plants have permanent N2O sensors installed in the biological reactors, and N2O data are collected on the same terms as other operational data. We present and deploy a six-dimensional data quality assessment to the operational data evaluating (1) relevance, (2) accuracy, (3) completeness, (4) consistency, (5) comparability, and (6) accessibility. To increase the accuracy and completeness of the stored data, it is suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. Furthermore, seasonal variations and time-varying relationships between N2O, nitrogenous variables, and oxygen are investigated and compared across various case plants and process designs. Results show that the quality of the operational data varies substantially between plants. The investigation of time-varying interrelation between N2O and nitrogenous variables showed no clear pattern within or across different case plants. Furthermore, it is recommended that future research should consider adapting models so that more influence is linked to reliable measurements, contrary to assuming that all variables are of equal quality.

The variability of NO2 concentrations over China based on satellite and influencing factors analysis during 2019–2021

The variation of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) indirectly reflects the difference in pollution emissions from industrial production and transportation. Accurately analyzing its pollution sources and driving factors plays an important role in energy conservation, emission reduction, and air pollution reduction. NO2 concentration products of Sentinel-5P (Sentinel-5 Precursor) TROPOMI (TROPOspheric Monitoring Instrument) from 2019 to 2021 and Aura OMI (Ozone Monitoring Instrument) from 2009 to 2021, combined with China’s main energy consumption, the growth value of the industry, Gross Domestic Product (GDP), and other data were used to analyze the influencing factors of NO2 variations. Firstly, NO2 tropospheric vertical column densities (NO2 TVCDs) of China increased by 14.72% and 3.26% in 2021 and 2020 compared with the 2019. The secondary and tertiary industry and the national energy consumption increased synchronously, which was highly related to the increase in NO2 TVCDs. Secondly, the impact of COVID-19 (coronavirus disease 2019) on China’s industrial production and residents was mainly concentrated in the first quarter of 2020, which leading to a decline in the annual average NO2 concentration in densely populated areas in 2020 compared to the same period in 2019. The industrial production scale and production capacity has gradually recovered since April 2020, and the NO2 concentration has gradually reached or exceeded the level of the same period of 2019. Finally, atmospheric pollution prevention and control measures played a positive role in the decline of NO2 of China.

Air pollution status and attributable health effects across the state of West Bengal, India, during 2016-2021.

Air pollution is one of the most significant threats to human safety due to its detrimental health consequences worldwide. This study examines the air pollution levels in 22 districts of West Bengal from 2016 to 2021, using data from 81 stations operated by the West Bengal Pollution Control Board (WBPCB). The study assesses the short- and long-term impacts of particulate matter (PM) on human health. The highest annual variation of PM10 was noted in 2016 (106.99 ± 34.17 μg/m3), and the lowest was reported in 2020 (88.02 ± 13.61 μg/m3), whereas the highest annual variations of NO2 (μg/m3) were found in 2016 (35.17 ± 13.55 μg/m3), and lowest in 2019 (29.72 ± 13.08 μg/m3). Similarly, the SO2 level was lower (5.35 μg/m3) in 2017 and higher in 2020 (7.78 μg/m3). In the state, Bardhaman, Bankura, Kolkata, and Howrah recorded the highest PM10 concentrations. The monthly and seasonal variations of pollution showed higher in December, January, and February (winter season) and lowest observed in June, July, and August (rainy season). The southern part of West Bengal state has recorded higher pollution levels than the northern part. The short- and long-term health impact assessment due to particulate matter shows that the estimated number of attributable cases (ENACs) for incidence of chronic bronchitis in adults and prevalence of bronchitis in children were 305,234 and 14,652 respectively. The long-term impact of PM2.5 on human health ENACs for mortality due to chronic obstructive pulmonary disease for adults, acute lower respiratory infections in children aged 0-5, lung cancer, and stroke for adults were 21,303, 12,477, 25,064, 94,406, and 86,272 respectively. This outcome assists decision-makers and stakeholders in effectively addressing the air pollution and health risk concerns within the specified area.

Trend Analysis of Temporal Variations in NO2 in Punjab Region

Trend Analysis of Temporal Variations in NO2 in Punjab Region

A global insight on sensitivity of nitrate leaching to drainage in arable cropping systems

Nitrate (NO3–) leaching leads to widespread deterioration of water quality. NO3– leaching can be regulated by both biochemical and hydrological processes. However, the extent to which drainage accounts for the variation in NO3– leaching and the sensitivity of NO3– leaching to drainage remain unclear, particularly at the global scale. This raises uncertainties in prioritizing the regulation of hydrological (e.g., drainage) processes for reduced NO3– leaching. A global meta-analysis was conducted for arable cropping systems to test two hypotheses: (1) Global variations in NO3– leaching are explained more by drainage and associated influencing factors (e.g., water input); (2) NO3– leaching is more sensitive to the changes in drainage under conditions with lower risks of preferential flow. In this meta-analysis, the average NO3– leaching loss from arable cropping systems was 22.2 kg N ha−1 year−1. Water input, followed by N input, were the most influential in explaining global variations in NO3– leaching. Compared with NO3– concentration, drainage and associated influencing factors explained more of the global variations in NO3– leaching losses. NO3– leaching was more sensitive to drainage in soils with finer texture and higher soil organic carbon content, in regions with low to medium mean annual temperature (<15 ℃) and medium mean annual precipitation (600–1000 mm), where water input during the measurement period was low (<2 mm day−1), nitrogen fertilizer rates were high (>200 kg N ha−1), and where conservation tillage systems (no-tillage or non-inversion tillage) were adopted. Therefore, more attention should be given to avoiding excessive water application in soils characterized by low risk of preferential flow. Preferential flow reduces the chance for drainage water to interact with NO3– in the soil matrix, particularly in areas with high N inputs. While this study has successfully tested our hypotheses, it further highlights the importance of water input management for mitigating global NO3– leaching risks, particularly under environments and changing climate conditions more conducive to drainage events.

Modeling NO2 air pollution variation during and after COVID-19-regulation using principal component analysis of satellite imagery

By implementing Principal Component Analysis (PCA) of multitemporal satellite data, this paper presents modeling solutions for air pollutant variation in three scenarios related to COVID-19 lockdown: pre, during, and after lockdown. Tropospheric NO2 satellite data from Sentinel-5P was used. Two novel PCA-models were developed: Weighted Principal Component Analysis (WPCA) and Rescaled Principal Component Analysis (RPCA). Model results were tested for goodness-of-fit to empirical NO2 data. The models were used to predict actual near-surface NO2 concentrations. Model-predicted NO2 concentrations were validated with NO2 data acquired at ground monitoring stations. Besides, meteorological bias affecting NO2 was assessed. It was found that the weather component had substantial impact on NO2 built-ups, propitiating air pollutant decrease during lockdown and increase after. WPCA and RPCA models well fitted to observed NO2. Both models accurately estimated near-surface NO2 concentrations. Modeled NO2 variation results evidenced the prolongated effect of the total lockdown (up to half a year). Model-predicted NO2 concentrations were found to highly correlate with monitoring station NO2 data collected on the ground. It is concluded that PCA is reliable in identifying and predicting air pollution variation patterns. The implementation of PCA is recommended when analyzing other pollutant gases.