NO Values Research Articles

Solving the challenges of interpolating NO<sub>2</sub> from SPRINT data and modelling population movements in agent-based modelling

This study addresses two critical challenges in urban air quality exposure simulation and offers solutions. The first challenge is to generate nitrogen dioxide (NO2) fields across the city of London from available stations that provide Spatially Poor but Rich In Time (SPRINT) data. We first used Inverse Distance Weighting (IDW) to spatially interpolate NO2 at each half-a-day step. Each station had a list of hourly NO2 values for each time step, from which one NO2 value was selected by a stochastic process to generate the field. We also added weightings of up to a factor of 3 to London's NOx emissions to account for emissions from sources other than vehicles. We cross-validated the modelled data with the station data and found beta parameter of 1.5 to be the most appropriate 'power' parameter. The second challenge investigated the use of a fractional origin-destination (OD) matrix to see how to overcome errors when assigning destinations to a small set of population. We tested that ‘the nested bin strategy’ worked well for our 6,078 London resident agents. To enrich the dynamics to represent people’s non-work mobility patterns, we included visits to recreational areas during weekends and festive periods. This, in turn, provides a more comprehensive representation of urban mobility. Solutions to each challenge can provide more accurate assessments of pollution exposure, leading to better informed public health interventions.

Temperature Sensitivity of Soil Nitrous Oxide Emissions in Tea Plantations at Different Elevations

To solve the problems that the response of nitrous oxide （N2O） emissions to warming may be different in different biological communities and the uncertainty of its response to warming may be aggravated by the high heterogeneity of tea plantation soils, an incubation experiment at five elevations of tea plantation soils [537 m（E1）, 433 m（E2）, 350 m（E3）, 308 m（E4）, and 238 m（E5）] and three temperatures （15, 25, and 30 ℃） was conducted for 35 days to explore the temperature sensitivity of N2O in different tea plantation soils and the driving factors. The results showed that elevated temperature could promote soil N2O emissions at different elevations, but the effect was inconsistent at different elevations. Specifically, elevated temperature decreased the cumulative N2O emissions in the E1 treatment, but the difference was not significant. At the same time, the cumulative N2O emissions in the E2-E5 treatments were increased, especially in the E3-E5 treatments. The Q10 values of N2O in the tea plantation soils at different elevations were significantly different, ranging from 0.90 to 4.98, with E4 > E5 > E3 > E2 > E1. The multiple regression model analysis showed that 97.4% of the variation in N2O temperature sensitivity was explained by soil physicochemistry and microorganisms, and N2O temperature sensitivity was significantly positively correlated with bacterial beta diversity PCoA1. In conclusion, increasing temperature can promote the N2O emissions in tea plantation soils at different elevations, but there is no consistent change rule between elevation and N2O temperature sensitivity, and N2O temperature sensitivity is mainly affected by the bacterial beta diversity.

Isotopic Signatures and Fluxes of N2O Emitted from Soybean Plants and Soil During the Main Growth Period of Soybeans

Soil microorganisms have long been recognized as primary producers of biogenic N2O in terrestrial ecosystems. Terrestrial plants can contribute to N2O emissions by transporting N2O produced in soils, and there is also evidence that plants may serve as direct producers of N2O. However, to date, direct evidence for N2O production by plants remains limited. To exclude N2O emissions resulting from soil-to-plant transport, this study conducted incubation experiments using cut soybean branches and leaves (cSBF) and intact soil cores under an N2O-free air background. The natural isotopic signatures (δ15N and δ18O) and fluxes of N2O produced by cSBF and soil were compared across different soybean growth stages over two growing seasons. The observed δ15N and δ18O values of N2O from soil ranged from −26.7‰ to −5.3‰ and −24.1‰ to 22.8‰, respectively. In contrast, the values for N2O produced from cSBF ranged from −4.7‰ to 33.1‰ and from 23.7‰ to 88.8‰, respectively. Notably, N2O emitted from plants exhibited significantly higher δ15N and δ18O values than soil-derived N2O (p < 0.05). These findings indicate that the pathways and mechanisms of N2O production and emission in soybean plants differ from those mediated by soil microorganisms and nitrogen transport processes. Additionally, a significantly higher amount of N2O emission was observed during early growth stages compared to late growth stages (p < 0.01), suggesting that plant N2O production may be associated with elevated water content and oxygen-limited conditions within plant cells. In addition to the N2O uptake by plants observed in some literature, the positive relationship between δ15N values and N2O fluxes suggests that N2O could be consumed in plant cells (p < 0.01), with a high consumption rate often associated with a high production rate. The results of this study provide compelling evidence that plants may represent an overlooked source of N2O in terrestrial ecosystems.

Assessing the impact of land cover on air quality parameters in Jordan: A spatiotemporal study using remote sensing and cloud computing (2019–2022)

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively. The Google Earth Engine (GEE) platform and JavaScript were used to produce monthly short-term average concentration maps and time series for the three pollutants. Pearson correlation analysis was performed to evaluate the performance of Sentinel-5P data against ground-based monitoring stations in estimating NO2, SO2, and CO concentration at a regional scale. Results revealed a moderate correlation, with r-values of 0.42, 0.43, and 0.40, for NO2, SO2, and CO, respectively. The spatiotemporal analysis showed a higher concentration of SO2 and NO2 in the northern and middle regions of the country, coinciding with the spatial distribution of built-up areas and the main urban centers. On a temporal scale, the highest concentration of the three pollutants was observed in the winter months for all governorates of Jordan. For instance, it was found that the highest value of NO2 was in Balqa Governorate in December 2022, 1.57 * 10^4 mol/m2. The highest average monthly SO2 values were observed in Jerash Governorate in December 2019, 7.36 * 10^4 mol/m2. CO concentrations were mainly concentrated in the western parts of the Jordan rift valley.

NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components

Abstract. Identifying PM2.5 chemical components is crucial for formulating emission strategies, estimating radiative forcing, and assessing human health effects. However, accurately describing spatiotemporal variations in PM2.5 chemical components remains a challenge. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v1.0) that was suboptimal for chemical components. This paper introduces a novel hybrid nonlinear chemical DA system (NAQPMS-PDAF v2.0) to accurately interpret key chemical components (SO42-, NO3-, NH4+, OC, and EC). NAQPMS-PDAF v2.0 improves upon v1.0 by effectively handling and balancing stability and nonlinearity in chemical DA, which is achieved by incorporating the non-Gaussian distribution ensemble perturbation and hybrid localized Kalman–nonlinear ensemble transform filter with an adaptive forgetting factor for the first time. The dependence tests demonstrate that NAQPMS-PDAF v2.0 provides excellent DA results with a minimal ensemble size of 10, surpassing previous reports and v1.0. A 1-month DA experiment shows that the analysis field generated by NAQPMS-PDAF v2.0 is in good agreement with observations, especially in reducing the underestimation of NH4+ and NO3- and the overestimation of SO42-, OC, and EC. In particular, the Pearson correlation coefficient (CORR) values for NO3-, OC, and EC are above 0.96, and the R2 values are above 0.93. NAQPMS-PDAF v2.0 also demonstrates superior spatiotemporal interpretation, with most DA sites showing improvements of over 50 %–200 % in CORR and over 50 %–90 % in RMSE for the five chemical components. Compared to the poor performance in the global reanalysis dataset (CORR: 0.42–0.55, RMSE: 4.51–12.27 µg m−3) and NAQPMS-PDAF v1.0 (CORR: 0.35–0.98, RMSE: 2.46–15.50 µg m−3), NAQPMS-PDAF v2.0 has the highest CORR of 0.86–0.99 and the lowest RMSE of 0.14–3.18 µg m−3. The uncertainties in ensemble DA are also examined, further highlighting the potential of NAQPMS-PDAF v2.0 for advancing aerosol chemical component studies.

Estimation of Near-surface NO2 Concentration in Guangdong Province Based on Catboost Model

Nitrogen oxide （NOx） is an important air pollutant in the atmosphere, and nitrogen dioxide （NO2） is one of its main components. The monitoring and estimation of NO2 concentration is very important for environmental protection and public health. The near-real-time nitrogen dioxide concentration data （NRTI NO2）, ERA5 meteorological reanalysis data, and DEM data provided by Sentinel-5P atmospheric pollution monitoring satellite were used as estimation variables to estimate the near-surface NO2 concentration in Guangdong Province based on the Catboost model. The results showed that： ① The Catboost model estimated the near-surface NO2 concentration with the highest accuracy, with the coefficient of determination （R2）, root mean square error （RMSE）, and mean absolute error （MAE） of the model fit reaching 0.91, 4.89 μg·m-3, and 3.45 μg·m-3 and the cross-validated R2, RMSE, and MAE reaching 0.90, 4.91 μg·m-3, and 3.43 μg·m-3, with good stability on the monthly and quarterly scales. ② The monthly average NO2 concentration near the surface of Guangdong Province showed a U-shaped trend, with the highest value of 43.8 μg·m-3 in January and the lowest value of 14.37 μg·m-3 in July. The seasonal distribution of the near-surface NO2 concentration was characterized by "high during winter and low during summer and transitional during spring and autumn," and the NO2 concentration in each season was in the following order： winter （27.53 μg·m-3） > spring （20.77 μg·m-3） > autumn （18.77 μg·m-3） > summer （14.85 μg·m-3）. ③ From a spatial distribution perspective, areas with high near-surface NO2 values in Guangdong Province were mainly located in rapidly developing and densely populated areas, while areas with low values were mainly distributed in areas focusing on port economy, agriculture, and new energy sources.

Hydrochemical Characterisation of the Basement Aquifer with a Focus on the Origin of Nitrate in the Highly Urbanised Niamey Region, SW of Niger

This study investigated the basement aquifer beneath the urbanised city of Niamey and the agricultural fields of Kollo, SW of Niger. During the observation period spanning from 2021 to 2023, groundwater and surface water samples were collected for analysis of major ions and the stable isotopes oxygen-18 and deuterium (δ18O and δ2H) of water. To trace the origin of high nitrate concentrations (NO3−) found in several observation and drinking water wells in both areas, δ15N and δ18O isotope values of NO3− were analysed in groundwater and eluted soil samples. The observed hydrochemical patterns mainly reflect the heterogeneity of the weathered fringe of the basement aquifer. Decreasing concentrations of NO3− and δ18O and δ2H values were observed in relation to the distance of the Niger River and increasing thickness of the clay layer on the surface. The wells close to the river in Niamey show a dilution effect during the flood season, and the NO3− concentrations displayed a continuous increasing trend. The δ15N-NO3 and δ18O-NO3 values confirmed that septic tank water is spreading in the region of Niamey and that manure originating from livestock in Kollo is the main source of NO3−. The patterns of δ15N in the soil samples coincide with those of cattle’s manure spread in both areas. The shallow wells show significantly higher values of electric conductivity and NO3− concentrations compared to the deeper wells, which clearly indicates the influence of shallow septic tanks on water quality.

Exposure to tropospheric ozone and NO2 in the ambient air of Tehran metropolis: Spatiotemporal distribution and inhalation health risk assessment

BackgroundAir pollution has emerged as a critical challenge in the 21st century, necessitating alert monitoring of key pollutants such as ozone and nitrogen dioxide (NO2)F. These pollutants are exacerbated by urbanization and industrialization, posing significant health risks, particularly in densely populated metropolitan areas. ObjectiveThe aim of this study was inhalation health risk assessment of NO2 and ozone in various scenarios and spatiotemporal Distribution Interpolation. MethodsData were collected from the Tehran Air Quality Control Center, encompassing 18 monitoring stations from March 2019 to February 2022. Health risk assessments (HRA) were performed for adult age groups across three different exposure scenarios (3, 8, and 12 h) And a sensitivity analysis was also conducted to determine the relative importance of each parameter in assessing the risks of each scenario. ResultsThe hazard index (HI) values for NO2 and ozone in all three scenarios were found to range from 0.24 to 1.56 and 0.04 to 0.49, respectively and body weight is a risk-lowering factor in sensitivity analyses. The interpolation results of ozone, NOx, and NO2 using the IDW approach from March 2019 to February 2022 showed that overall, the north and northeast of Tehran had the highest concentrations of NO2 and NOx, and the north and west had the highest concentrations of ozone. SignificanceThis study underscores the HRA with exposure to NO2 and ozone, particularly for individuals spending more than 8 h outdoors. Given Tehran's high population density and persistent air pollution, it is imperative to implement effective control policies to safeguard public health. The findings advocate for limiting outdoor activity to less than 8 h per day, especially for vulnerable occupational groups. Additionally, the study highlights the importance of considering body weight in health risk assessments.

Hydro-geochemistry and age-dependent health risk assessment of nitrate, nitrite, and fluoride in health facilities water: a multivariate analysis.

Groundwater from alluvial fan plains is the prevailing water source, especially for arid/semiarid regions, but its contamination poses substantial risks to water supply and public health. The recent study aims to assess the hydro-geochemistry, distribution, and potential health risks of NO3-, NO2-, and F- concentrations in the groundwater of previously unexplored health facilities in District Vehari, Punjab, Pakistan. In total, 75 groundwater samples were evaluated for NO3-, NO2-, and F- levels as well as pH, EC, TDS, CO32-, HCO3-, Cl-, Na+, Fe, K+, Ca2+, Mg2+, taste, odor, color, and turbidity. The Durav graph shows that the water type is Na-HCO3-Ca, with Na and HCO3 dominant, weak acids > strong acids, and alkaline ions > alkalis. Results revealed that drinking water samples (21.73% and 20%) taken from Tehsil Mailsi, and the Basic Health Unit (BHU) exceeded the WHO standard (1.5mg/L) for F- concentration, respectively. Moreover, the mean chronic daily intake (CDI) of F- was 0.044, 0.018, and 0.02mg/kg/day in children, men, and women, respectively. Similarly, the average CDI of NO3- was 0.113, 0.046, and 0.050 in children, men, and women, respectively, and the respective values of NO2- were 0.004, 0.001, and 0.001. The NO2- shows a significant range of hazard quotient (HQ) (0.0-1.172) in children. The range of HQ for F- was 0.0-3.114, 0.0-1.290, and 0.0-1.389 in children, men, and women, respectively. Additionally, the health risks analysis revealed an HQ > 1.0 for children in groundwater, indicating a potential carcinogenic risk from the F-. Pearson correlation and PCA analysis found a significant positive correlation (0.8) between NO3- and NO2- and a negative correlation (0.3) between F- and HCO3-. These findings highlight the need for groundwater treatment in healthcare facilities prior to water consumption. Enforcing international and national drinking water standards in healthcare units is vital to strengthening services and providing equitable access to safe drinking water. Legislative and efficient water management measures must be taken for the protection of public health.

Surface water potential zones delineation and spatiotemporal variation characteristics of water pollution and the cause of pollution formation in Brahmani River Basin, Odisha

Waste water reuse is an effective method for reducing the quantity of wastewater that enters the environment. As a result, the appropriateness was assessed in accordance with its composition and the global criteria for drinking water quality. This study aims to evaluate the quality of surface water of Brahmani River, Odisha, by adapting multivariate statistical approaches, namely Cluster Analysis (CA) and multi-criteria decision-making (MCDM) methods such as Simple Additive Weighting (SAW). Again, Geographical Information System (GIS) is employed to assess the drinking water's quality in relation to its application. Around 15 physicochemical parameters, gathered from seven distinct sites, following a period of 2022 till 2024. The results showed high values of EC, SO42−, NO3−, PO43−, F− and Ca2+, in three stations, while other studied parameters are within the permissible limits defined by WHO standards. The CA three distinct classifications of sites namely, low, medium, and high, based on how comparable the various physicochemical traits and pollutant levels were throughout the sampling locations. Sites H-(4–6) were included in cluster 1, site H-(3) and (7) classified under Cluster 2 and finally, H-(1–2) indicated as Cluster 3. Thus, as stated by CA, 42.86 % of tested water samples were fall into the group of good quality and about 57.14 % of examined locations resides in the poor category of water. The findings indicate which characteristics, as determined by the CA technique, were accountable for the alteration in the water quality and raise the potential of runoff from cities, farms, and industries. Subsequently, MCDM techniques has been implemented to facilitate rapid comprehension of surface water behavior and address problems with water resource management. The obtained SAW index ranges from 0.5 to 0.94. Here, H-(1), (2), and (7) grouped as highly polluted zone, and they seemed to be connected to runoff from farms and untreated industrial and municipal trash as well as local pollution sources. From this innovative study, it is found that even though the water is fit for drinking at 3 places, it is medium to highly suitable for human consumption. Therefore, there needs to be treated in some way before being used in addition to being protected against contamination. Most often, the findings indicate that appropriate management actions are important to restore the water quality of this catchment in order to support a robust and promising aquatic ecosystem. Additionally, the significance of this restoration for objective ecological policy and decision-making processes is underscored.

Spatiotemporal evolution of the physicochemical parameters of the waters of the Oum Errabiaa watershed dams (Morocco)

This study was conducted to determine the characteristics of the physicochemical parameters of the water of the dams of the Oum Errabiaa watershed during flood and low water periods. Data were collected by water sampling and measurements performed in the various dams from 2016 to 2021. The results showed that the water temperature varied from 11.77 °C to 30.20 °C, depending mainly on the sunshine. The water was alkaline with an average pH of 8.23 and had a weak to high electrical conductivity (143–3180 µS/cm). The water was relatively well oxygenated at the surface and oxygen depleted in depth (1.44 – 15.04 mg/L). The average concentration of suspended solids (SS) was 19.76 mg/L, indicating that the dam water is relatively unpolluted. The average values of nutrients, NH4+, NTK and NO3- of 0.18 mg/L, 0.53 mg/L and 3.51 mg/L, respectively, were in compliance with the WHO guidelines for drinking water production. The average concentration of SO42- was 90.60 mg/L, which is considered high, exceeding the World Health Organization (WHO) water quality standard of 50 mg/l. The average chlorophyll a was 5.51 µg/L, which is considered to be low to moderate in dams. Overall, the Oum Errabiaa’s water dams of watershed have good physicochemical quality, except for the high concentration of sulfates. This pollution is likely due to agricultural and industrial activities in the watershed. The use of multivariate techniques (ACPN) enabled us to define the origin of nutrients through surface inputs, on the one hand, and through the photosynthetic activity of cyanobacteria, on the other hand.

Investigating the Relationship between Urbanization and Air Pollution Using Google Earth Engine Platform: A Case Study of Istanbul

Rapid population growth in megacities such as Istanbul has led to various effects, such as industrialization, urbanization, loss of green areas, increasing vehicle traffic, and higher consumption of fossil fuels. These reasons, along with many other environmental factors, contribute to the rise of air pollution in urban life. This study aimed to examine the relationship between urbanization and air pollution in Istanbul. For this purpose, land cover maps covering Istanbul province were produced using Landsat-5 (TM), Landsat-8 (OLI), and Sentinel-2 (MSI) images for the years 1996 to 2021 at three-year intervals on the Google Earth Engine platform. Land cover for classification purposes was divided into five different classes: forest, water surface, urban area, and bare land, and classified using a random forest machine learning algorithm. To examine the impact of this urban area growth on air pollution, in the second step of the study, the column number density values of Sentinel 5P (TROPOMI) data for SO2, NO2, CO, and O3 gases for 2019, 2020, and 2021 were analyzed. The averages of the data from 39 air pollution monitoring stations across Istanbul were also examined. According to this classification, the urban area expanded from 491 km2 in 1996 to 1222 km2 by 2021. Considering the total surface area of Istanbul province, the urban area, which was 9% in 1996, reached 23% by 2021. The TROPOMI values were calculated as follows: the average column number density values for SO2, NO2, CO, and O3 were 0.0003538 mol/m², 0.0339514 mol/m², 0.0000984 mol/m², and 0.1453515 mol/m², respectively. Similarly, the gas concentrations of SO2, NO2, CO, and O3 measured from the ground stations were calculated as 6.603 µ/m3, 786,815 µ/m3, 43.763 µ/m3 and 45.773 µ/m3, respectively. Correlations between urbanization and TROPOMI values revealed a positive correlation of 0.39, 0.02, and 0.80 for SO2, NO2, and CO gases, while a negative correlation of 0.25 was found for O3 gas. The study also examined correlations between TROPOMI and ground station measurements, resulting in positive correlations of 0.55, 0.66, and 0.16 for SO2, NO2, and CO gases, respectively, while a negative correlation of 0.05 was found for O3 gas. Based on these findings, among the air pollutants studied both through TROPOMI and ground station data, the highest correlation was observed for CO gas.

Impact of construction activities on the air quality of Agra city, Uttar Pradesh, India

The current research work was carried out to comprehensively assessed the impact of construction activities on the air quality. Seven sites were chosen along the metro line construction in Agra, Uttar Pradesh, India, to meet the study's goals. The monitoring was performed for 24 hours at each site using the respirable dust sampler (RDS) with a gaseous sampling attachment. The raw data was processed to calculate the Air Quality Index (AQI). The data obtained indicate that all the examined sites had PM10 (particulate matter having the diameter less than or equal to 10 micron) values above the National Ambient Air Quality Standards (NAAQ) values of 100 µg/m3, while SS-05 and SS-06 had PM2.5 (particulate matter having the diameter less than or equal to 2.5 micron) values above the NAAQ values of 60 µg/m3. Values of CO, SO2, and NO2 were discovered to be lower than the NAAQ standard limits. Because PM10's sub index (Si) was found to be the greatest across all locations, it was determined to be the criterion pollutant among all the metrics. Based on the AQI value, the research area's overall air quality was determined to be moderately polluted. At every location, a variety of management techniques, including mist guns, water spraying, and planting, are regularly used to reduce air pollution. Effective implementation of applied air pollution control measures is required to make the air clean and safe for breathing.

Assessment of the impact of NO2 contribution on aerosol-optical-depth measurements at several sites worldwide

Abstract. This work aims at investigating the effect of NO2 absorption on aerosol-optical-depth (AOD) measurements and Ångström exponent (AE) retrievals of sun photometers by the synergistic use of accurate NO2 characterization for optical-depth estimation from co-located ground-based measurements. The analysis was performed for ∼ 7 years (2017–2023) at several sites worldwide for the AOD measurements and AE retrievals by Aerosol Robotic Network (AERONET) sun photometers which use OMI (Ozone Monitoring Instrument) climatology for NO2 representation. The differences in AOD and AE retrievals by NO2 absorption are accounted for using high-frequency columnar NO2 measurements by a co-located Pandora spectroradiometer belonging to the Pandonia Global Network (PGN). NO2 absorption affects the AOD measurements in UV-Vis (visible) range, and we found that the AOD bias is the most affected at 380 nm by NO2 differences, followed by 440, 340, and 500 nm, respectively. AERONET AOD was found to be overestimated in half of the cases, while also underestimated in other cases as an impact of the NO2 difference from “real” (PGN NO2) values. Overestimations or underestimations are relatively low. About one-third of these stations showed a mean difference in NO2 and AOD (at 380 and 440 nm) above 0.5 × 10−4 mol m−2 and 0.002, respectively, which can be considered a systematic contribution to the uncertainties in the AOD measurements that are reported to be of the order of 0.01. However, under extreme NO2 loading scenarios (i.e. 10 % highest differences) at highly urbanized/industrialized locations, even higher AOD differences were observed that were at the limit of or higher than the reported 0.01 uncertainty in the AOD measurement. PGN NO2-based sensitivity analysis of AOD difference suggested that for PGN NO2 varying between 2 × 10−4 and 8 × 10−4 mol m−2, the median AOD differences were found to rise above 0.01 (even above 0.02) with the increase in NO2 threshold (i.e. the lower limit from 2 × 10−4 to 8 × 10−4 mol m−2). The AOD-derivative product, AE, was also affected by the NO2 correction (discrepancies between the AERONET OMI climatological representation of NO2 values and the real PGN NO2 measurements) on the spectral AOD. Normalized frequency distribution of AE (at 440–870 and 340–440 nm wavelength pair) was found to be narrower for a broader AOD distribution for some stations, and vice versa for other stations, and a higher relative error at the shorter wavelength (among the wavelength pairs used for AE estimation) led to a shift in the peak of the AE difference distribution towards a higher positive value, while a higher relative error at a lower wavelength shifted the AE difference distribution to a negative value for the AOD overestimation case, and vice versa for the AOD underestimation case. For rural locations, the mean NO2 differences were found to be mostly below 0.50 × 10−4 mol m−2, with the corresponding AOD differences being below 0.002, and in extreme NO2 loading scenarios, it went above this value and reached above 1.00 × 10−4 mol m−2 for some stations, leading to higher AOD differences but below 0.005. Finally, AOD and AE trends were calculated based on the original AERONET AOD (based on AERONET OMI climatological NO2), and its comparison with the mean differences in the AERONET and PGN NO2-corrected AOD was indicative of how NO2 correction could potentially affect realistic AOD trends.

Study of Ion-to-Electron Transducing Layers for the Detection of Nitrate Ions Using FPSX(TDDAN)-Based Ion-Sensitive Electrodes.

The development of ISE-based sensors for the analysis of nitrates in liquid phase is described in this work. Focusing on the tetradodecylammonium nitrate (TDDAN) ion exchanger as well as on fluoropolysiloxane (FPSX) polymer-based layers, electrodeposited matrixes containing double-walled carbon nanotubes (DWCNTs), embedded in either polyethylenedioxythiophene (PEDOT) or polypyrrole (PPy) polymers, ensured improved ion-to-electron transducing layers for NO3- detection. Thus, FPSX-based pNO3-ElecCell microsensors exhibited good detection properties (sensitivity up to 55 mV/pX for NO3 values ranging from 1 to 5) and acceptable selectivity in the presence of the main interferent anions (Cl-, HCO3-, and SO42-). Focusing on the temporal drift bottleneck, mixed results were obtained. On the one hand, relatively stable measurements and low temporal drifts (~1.5 mV/day) were evidenced on several days. On the other hand, the pNO3 sensor properties were degraded in the long term, being finally characterized by high response times, low detection sensitivities, and important measurement instabilities. These phenomena were related to the formation of some thin water-based layers at the polymer-metal interface, as well as the physicochemical properties of the TDDAN ion exchanger in the FPSX matrix. However, the improvements obtained thanks to DWCNT-based ion-to-electron transducing layers pave the way for the long-term analysis of NO3- ions in real water-based solutions.

453 Fluxes of greenhouse gases from pastures and methane emissions from beef cattle in Australia

Abstract The carbon footprint of beef cattle production may need to consider both the fluxes of greenhouse gases (GHG) of pasture ecosystems and cattle enteric emissions because these are the largest contributors to the carbon balance. The objective of the present study was to measure the fluxes of greenhouse gases from pastures and enteric methane (CH4) emissions from cattle throughout long periods of time under commercial conditions. The fluxes of CH4, carbon dioxide (CO2), and nitrous oxide (N2O) were measured throughout a period of 3 yr in multiple pasture types ranging from native to sown, and from temperate to summer pastures in New South Wales, Australia. A set of 8 dynamic chambers that automatically close for 10 min every 1.5 h were used for this purpose. The chambers measured the change in the concentration of GHG to allow for the estimation of emissions (positive flux) or sinks (negative flux). All data was then summarized to estimate daily fluxes and the global warming potential (CO2e) was calculated multiplying CH4 by 28 and N2O by 265. The final dataset contained 2,709 d of measurements across the 8 chambers. The GreenFeed system was used to measure CH4 production from cattle under varying conditions fed various diets throughout the same period. Cattle categories included weaners, heifers and steers, bulls and cows under both grazing or pen conditions. Cattle were fed a variety of diets ranging from low quality forages to high quality forages, grain supplementation, and grain-based diets. Data were averaged across many days for each animal and the final dataset contained 1,014 measurements. The CO2 flux from pastures ranged from -855 (uptake) to +339 kg CO2ּ ha-1ּ d-1 with a mean of -10.4 ± 2.19 kg CO2ּ ha-1ּ d-1. The minimum, mean and maximum CH4 fluxes were -192 (uptake), 0.015 ± 0.488, 434 g CH4 ּha-1ּ d-1, respectively, whereas NO2 values -146 (uptake), 17.1 ± 1.23, 1,293 g N2O ּ ha-1ּ d-1, respectively. These fluxes resulted in a GWP ranging from -855 (uptake), -5.83 ± 2.222, 445 kg CO2e ּha-1ּ d-1suggesting that pastures were on average a net sink of GHG throughout the present study. The CH4 production from cattle ranged from 43.6 to 404.6, with an average CH4 production of 192 ± 90.3 g CH4ּ animal-1ּ d-1, which is equivalent to 5.37 kg CO2eqּ animal-1ּ d-1. These results suggest that a hectare of livestock pastures offsets more than the CH4 produced by the average beef cattle in the present study. The fluxes of GHG from vegetation may need to be considered when estimating the contribution of livestock production to global warming.

Fractionation of Sediment-Bound Nitrogen: Bioavailability and Contamination Status of the Nutrient in Thrissur Kole Wetlands, Southwest India

ABSTRACT Fractionation of sediment-bound nitrogen (N) in Thrissur Kole wetland was carried out to assess the mobility, bioavailability, and nutrient contamination status. Various N fractions were extracted using KCl equilibrium extraction method, and the concentration of each N fraction was analyzed by spectrophotometric method. Dominance of ammonia during all seasons was due to dissimilatory NO3 − reduction to NH4 + and direct inputs from agriculture fields. Enrichment of urea during post-monsoon (181.78 ± 8.47 ppm) was attributed to fertilizer sources. NO3 −-N deficit at central zone of the study area (monsoon and post-monsoon) was due to the anoxia. Comparatively higher values of NO2 −-N during monsoon (0.07 ± 0.02 ppm) was due to the land run-off, leaching of fertilizers from agriculture fields, and higher rate of denitrification. Elevated content of total N [1819.94 ± 40.20 ppm (pre-monsoon); 3061.62 ± 13.21 ppm (monsoon); 2763.44 ± 9.65 ppm (post-monsoon)] reflected the inputs from in-situ production, agriculture, terrestrial run-off, waste disposal, and guano as well as carcass of birds. Average contribution of bioavailable N to total N was higher (36.16%) indicated the possibility for eutrophication risk during pre-monsoon. Agriculture dominated regions recorded severe contamination and eutrophication risk as indicated by organic pollution index, organic nitrogen index, and phosphorous pollution index.

Assessment of ambient air quality of Lucknow city post monsoon 2023

This study monitoring and assessment of air pollutants, including lead and nickel trace metals, gases such as sulphur dioxide (SO2) and nitrogen dioxide (NO2), and respirable suspended particulate matter (PM10) and fine particulate matter (PM2.5), were done at nine locations that were divided into three categories: residential, commercial, and industrial of the ambient air quality in the city of the post-monsoon. With an average of 148.9 µg m-3, the 24-hour concentrations of PM10 ranged from 58.5 to 337.8 µg m-3, whereas the 24-hour concentrations of PM2.5 ranged from 32.5 to 118.4 µg m-3, with an average of 70.5 µg m-3. At every location, the mean concentrations of PM10 exceeded the allowable threshold of 100 µg m-3. While PM2.5 levels, with the exception of Aliganj, Vikas Nagar, and Chowk, exceeded the 60µg m-3 national limit. Pb and Ni, which are PM10 trace elements, range from 0.0028 to 0.0370 µg m-3, with an average of 0.0147 µg m-3 for Pb and 1.30 to 11.75 µg m-3, with an average of 6.62 µg m-3 for Ni. During a 24-hour period, the levels of SO2 varied between 7.9 and 57.7 µg m-3, with an average of 20.8 µg m-3. Conversely, the levels of NO2 varied between 9.3 and 89.0 µg m-3, with an average of 37.8. Between µg m-3 the post-monsoon of 2022 and 2023, SO2 concentrations declined by 16.0% and NO2 values increased by 22.0%, respectively. On the other hand, average SO2 and NO2 levels were significantly lower than the 80µg m-3permitted limits set by the CPCB (NAAQS-2009).

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.

Carbon to Nitrogen Ratio in Forage Plants in South Kalimantan's Oil Palm-Cattle Integration Area

The main cause of climate change is confirmed to originate from greenhouse gas emissions from human activities. Human activities such as burning fossil fuels, land use changes, population growth, and the accumulation of greenhouse gases. This research aims to determine the species of plants, organic C/N content, and to compare N2O emissions in oil palm plantations integrated with cattle farming. The research method used a survey approach in two sampling areas: oil palm plantations integrated with cattle and oil palm plantations without cattle integration. In each location, 5 plots were created, with each plot consisting of 3 subplots as replications from the edge towards the oil palm plantation, resulting in a total of 30 subplots for both locations. Parameters collected included plant species, organic C/N content, and the amount of N2O emissions. The research results showed that there are 5 plant species growing in both integrated and non-integrated oil palm plantation areas: Ageratum conyzoides, Panicum brevifolium , Centotheca longilamina Ohwi, Brachiaria decumbens dan Athyrium filex femina. Integrated and non-integrated land areas significantly affect carbon storage in both leaves and stems. Integrated land has a C/N ratio in leaves of 30.15-40.86%, while in non-integrated areas, it is 27.95-34.15%, and in stems, it ranges from 38.07-55.25% and 32.15-48.46% of total dry biomass. The average N2O gas emissions in non-integrated land are 4.6 N m²/hour, while in integrated oil palm plantations with cattle, it is 5.6 N m²/hour. The difference in N2O values between the two locations is within reasonable limits. In conclusion, oil palm plantations can be integrated with cattle for sustainable agriculture