Distribution Of Nitrogen Dioxide Research Articles

The Dynamics of Air Pollution in the Southwestern Part of the Caspian Sea Basin (Based on the Analysis of Sentinel-5 Satellite Data Utilizing the Google Earth Engine Cloud-Computing Platform)

The Caspian region represents a complex and unique system of terrestrial, coastal, and aquatic environments, marked by an exceptional landscape and biological diversity. This diversity, however, is increasingly threatened by substantial anthropogenic pressures. One notable impact of this human influence is the rising concentration of pollutants atypical for the atmosphere. Advances in science and technology now make it possible to detect certain atmospheric pollutants using remote Earth observation techniques, specifically through data from the Sentinel-5 satellite, which provides continuous insights into atmospheric contamination. This article investigates the dynamics of atmospheric pollution in the southwestern part of the Caspian Sea basin using Sentinel-5P satellite data and the cloud-computing capabilities of the Google Earth Engine (GEE) platform. The study encompasses an analysis of concentrations of seven key pollutants: nitrogen dioxide (NO2), formaldehyde (HCHO), carbon monoxide (CO), ozone (O3), sulfur dioxide (SO2), methane (CH4), and the Aerosol Index (AI). Spatial and temporal variations in pollution fields were examined for the Caspian region and the basins of the seven rivers (key areas) flowing into the Caspian Sea: Sunzha, Sulak, Ulluchay, Karachay, Atachay, Haraz, and Gorgan. The research methodology is based on the use of data from the Sentinel-5 satellite, SRTM DEM data on absolute elevations, surface temperature data, and population density data. Data processing is performed using the Google Earth Engine cloud-computing platform and the ArcGIS software suite. The main aim of this study is to evaluate the spatiotemporal variability of pollutant concentration fields in these regions from 2018 to 2023 and to identify the primary factors influencing pollution distribution. The study’s findings reveal that the Heraz and Gorgan River basins have the highest concentrations of nitrogen dioxide and Aerosol Index levels, marking these basins as the most vulnerable to atmospheric pollution among those assessed. Additionally, the Gorgan basin exhibited elevated carbon monoxide levels, while the highest ozone concentrations were detected in the Sunzha basin. Our temporal analysis demonstrated a substantial influence of the COVID-19 pandemic on pollutant dispersion patterns. Our correlation analysis identified absolute elevation as a key factor affecting pollutant distribution, particularly for carbon monoxide, ozone, and aerosol indices. Population density showed the strongest correlation with nitrogen dioxide distribution. Other pollutants exhibited more complex distribution patterns, influenced by diverse mechanisms associated with local emission sources and atmospheric dynamics.

Air Pollution in the Port City of Lithuania: Characteristics of the Distribution of Nitrogen Dioxide and Solid Particles When Assessing the Demographic Distribution of the Population

Air Pollution in the Port City of Lithuania: Characteristics of the Distribution of Nitrogen Dioxide and Solid Particles When Assessing the Demographic Distribution of the Population

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Vertical Characteristics of HCHO and NO2 in Suburban Shanghai: Understanding of Ozone formation sensitivity via Secondary HCHO

Abstract Since ozone (O3) pollution in China is on the rise, vertical distribution of nitrogen dioxide (NO2) and formaldehyde (HCHO) and their role towards O3 formation are critical towards designing O3 control strategies. Seasonal characteristics of aerosols, HCHO and NO2 were investigated over suburban Shanghai using Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) from June 2020 to May 2021. For HCHO, the highest average vertical mixing ratios (VMRs) were observed for summer (0.775 ppb), followed by autumn (0.719 ppb), spring (0.718 ppb) and winter (0.672 ppb) at ground level. Seasonal trends in NO2 VMRs were observed to be opposite to that of HCHO with peak values occurring during winter (2.212 ppb) at the ground level followed by autumn (1.881 ppb), spring (1.415 ppb) and summer (1.124 ppb). In vertical, aerosols depicted a Gaussian shape with a higher values between 0.2 - 0.5 km. An exponential decay in NO2 VMRs is observed from 0.0 km to 3.0 km while the integral component of HCHO was found to be concentrated up to 2.0 km particularly during summer months. The vertical distribution of NO2 in different seasons mainly appeared below 0.5 km, while that of HCHO appeared within the range of 0.2-1.0 km. Both aerosols and NO2 tend to be higher during haze days compared to non-haze days while HCHO was found to be higher during clear days owing to high photochemical activity on a clear day. Based on the ratio of HCHO to NO2 (RFN), this study attempted to quantify the vertical characteristics of O3 sensitivity for different seasons. The results indicated that the O3 sensitivity was VOC-limited both at lower (generally, 0.0 - 0.6 km) and higher heights (1.8 - 3.0 km) while some transition limited scenarios were found at middle height (0.7 – 1.7 km) particularly during daytime. The results depicted that secondary HCHO is a crucial factor controlling on O3 sensitivity over Shanghai where enhanced ratio of VOC limited regime was observed with secondary HCHO for all the seasons.

Assessing the impact of a waste incinerator on the environment using the MAIAC-AOD and AERMOD models

The distribution and concentration of air pollutants from infectious waste incineration between 2019 and 2021 were examined in this study using an AERMOD model, including sulfur dioxide (SO2), nitrogen dioxide (NOx), and particulate matter (PM2.5). The MAIAC-AOD value at a 1 km resolution was used to develop a regression model with meteorological information for predicting PM2.5, which was then compared with the concentration from the AERMOD simulation. The following maximum 1-h, 24-h, and annual average concentrations of all pollutants were found to have occurred in 2019. The distribution of SO2 and NOx in 1 h was largest in 2020 at 1,000 m to the northwest, with concentrations of 37.68 and 93.99 μg/m3, respectively. The 24-h concentrations of SO2 and NOx were greatest in 2021 at 3.63 and 8.90 μg/m3, respectively, 720 m northeast of the stack. The annual concentrations of SO2 and NOx were highest in 2019 at 0.56 and 1.36 μg/m3, respectively. The highest annual PM2.5 concentration was 0.033 μg/m3, 730 m to the northeast in 2019. The predicted PM2.5 using MAIAC-AOD correlated with the simulated value from AERMOD, with R2 values of 0.7630, 0.7607, and 0.6504 for 2019, 2020, and 2021, respectively, which were higher closer to the stack than outside. As a result, investigations into the distribution of air pollution should benefit from the integration of air modeling and satellite information.

Monitoring Nitrogen Dioxide (NO2) in Environment of Ukraine based on Satellite Data

Air pollution (especially near industrial enterprises that are located mainly in densely populated regions) is one of the most significant problems of modern ecology. The purpose of this research is to study nitrogen dioxide air pollution over Ukraine, which has a negative impact on human health. As part of the research over the territory of Ukraine, the real planar distribution of nitrogen dioxide (NO2) as well as its local emissions (which make the main contribution to this distribution) were revealed using the materials of the remote sensing of the Earth from the AURA satellite. The results were calculated for the multi-year period of 2005 through 2021 and separately for 2022, which characterized the full-scale war in Ukraine and which made it possible to identify priority polluters; namely, industrial enterprises (thermal power plants, heavy metallurgy enterprises, etc.). For 17 years, the average value of NO2 was 160.78 · 102 molecules/mm2; in 2022, its concentration decreased to 126.93·109 molecules/mm2. The war manifested itself due to the shutdown of industrial enterprises, which were (and remain) priority polluters in Ukraine (particularly in large cities).

Characterization of Nitrogen Dioxide Variability Using Ground-Based and Satellite Remote Sensing and In Situ Measurements in the Tiber Valley (Lazio, Italy)

The spatial–temporal distributions of nitrogen dioxide (NO2) in a rural area of Tiber valley were evaluated over one year (March 2022–February 2023) using remote sensing and in situ measurements. Surface concentration monitoring was conducted using a Pandora-2s spectrometer and a chemiluminescence analyzer operated at the Liberti Observatory (CNR-IIA). In spring, when the growing season and the agricultural activities increase, NO2 peaks were detectable by the Pandora but not by the in situ analyzer. The tropospheric Pandora and TROPOMI VCD products showed similar temporal patterns as those of the analyzer at the Observatory. High TROPOMI VCD levels in spring were detected at the Observatory and at six sites selected as representative of rural, residential, and industrial environments. WRF simulations found that high pollution events, observed by the Pandora and analyzer, occurred in calm wind conditions, favouring the accumulation of NO2 locally emitted. The complementary dataset provided by remote sensing and in situ techniques efficiently captured the spatial–temporal NO2 variability in a rural site exposed to low emission sources, thus supporting future decisional policies and actions.

Modeling Based on the Analysis of Interval Data of Atmospheric Air Pollution Processes with Nitrogen Dioxide due to the Spread of Vehicle Exhaust Gases

The article deals with the issue of modeling taking into consideration nitrogen dioxide pollution of the atmospheric surface layer caused by vehicle exhaust gases. The interval data analysis methods were suggested. The method of identifying the mathematical model of the distribution of nitrogen dioxide as an atmospheric air pollutant based on the analysis of data with known measurement errors was proposed and grounded for the first time. The obtained mathematical model in the form of a difference equation is characterized by the guaranteed accuracy of forecasting nitrogen dioxide concentrations in a specified area of the city. It also adequately takes into account traffic changes which significantly reduces the costs of environmental control and monitoring. The proposed new model identification method is more effective in terms of computational time complexity compared to the known method and it is based on taking into account measurement errors which in the final case provides predictive properties of the model with guaranteed accuracy.

Impact Assessment of COVID-19 Lockdown on Vertical Distributions of NO2 and HCHO From MAX-DOAS Observations and Machine Learning Models.

Responses to the COVID‐19 pandemic led to major reductions on air pollutant emissions in modern history. To date, there has been no comprehensive assessment for the impact of lockdowns on the vertical distributions of nitrogen dioxide (NO2) and formaldehyde (HCHO). Based on profiles from 0 to 2 km retrieved by Multi‐AXis‐Differential Optical Absorption Spectroscopy observation and a large volume of real‐time data at a suburb site in Shanghai, China, four types of machine learning models were developed and compared, including multiple linear regression, support vector machine, bagged trees (BT), and artificial neural network. Ultimately BT model was employed to reproduce NO2 and HCHO profiles with the best performance. Predictions with different meteorological and surface pollution scenarios were conducted from 2017 to 2019, for assessing the corresponding impacts on the changes of NO2 and HCHO profiles during COVID‐19 lockdown. The simulations illustrate that the NO2 decreased in 2020 by 43.8%, 45.5%, and 44.6%, relative to 2017, 2018, and 2019, respectively. For HCHO, the lockdown‐induced situation presented the declines of 28.6%, 32.1%, and 10.9%, respectively. In the comparisons of vertical distributions, NO2 maintained decreasing at all altitudes, while HCHO decreased at low altitudes and increased at high altitudes. During COVID‐19 lockdown, the reduction of NO2 and HCHO from the variation of surface pollutants was dominated below 0.5 km, while the relevant meteorological factors played a more significant role above 0.5 km.

Land-Use Regression Modeling to Estimate NO2 and VOC Concentrations in Pohang City, South Korea

Land-use regression (LUR) has emerged as a promising technique for air pollution modeling to obtain the spatial distribution of air pollutants for epidemiological studies. LUR uses traffic, geographic, and monitoring data to develop regression models and then predict the concentration of air pollutants in the same area. To identify the spatial distribution of nitrogen dioxide (NO2), benzene, toluene, and m-p-xylene, we developed LUR models in Pohang City, one of the largest industrialized areas in Korea. Passive samplings were conducted during two 2-week integrated sampling periods in September 2010 and March 2011, at 50 sampling locations. For LUR model development, predictor variables were calculated based on land use, road lengths, point sources, satellite remote sensing, and population density. The averaged mean concentrations of NO2, benzene, toluene, and m-p-xylene were 28.4 µg/m3, 2.40 µg/m3, 15.36 µg/m3, and 0.21 µg/m3, respectively. In terms of model-based R2 values, the model for NO2 included four independent variables, showing R2 = 0.65. While the benzene and m-p-xylene models showed the same R2 values (0.43), toluene showed a lower R2 value (0.35). We estimated long-term concentrations of NO2 and VOCs at 167,057 addresses in Pohang. Our study could hold particular promise in an epidemiological setting having significant health effects associated with small area variations and encourage the extended study using LUR modeling in Asia.

Ammonium nitrate explosion at the main port in Beirut (Lebanon) and air pollution: an analysis of the spatiotemporal distribution of nitrogen dioxide.

An explosion of the ammonium nitrate (AN) stored at Beirut Port devastated the city on Tuesday 4 August 2020. Such an explosion produces pollutants such as nitrogen oxides (NOx). The most common NOx is nitrogen dioxide (NO2), which is present in the atmosphere due to natural and anthropogenic processes. The presence of NO2 is used as indicator of air pollution. However, the specific contribution of NO2 to air quality is uncertain due to the presence of other constituents, especially particulate matter (PM10). Research has shown that extended exposure to NO2 may result in serious health effects. This study investigated the impact of the explosion on NO2 levels in the atmosphere above Beirut and the surrounding area. NO2 data from the Sentinel-5P program were used to map the levels of NO2. Furthermore, ground-monitoring data were used to assess the levels of PM10 and ozone (O3) due to the evident association between these constituents and NO2. Results showed that NO2 levels were higher than before the blast. However, 7 days after the explosion, NO2 levels had returned to normal, while the levels of PM10 and O3 remained normal following the explosion. However, a slight increase in the daily average atmospheric pressure was noticed after the explosion, which was attributed to the decomposition of ammonium nitrate.

Spatial and temporal distribution characteristics of ground-level nitrogen dioxide and ozone across China during 2015–2020

In recent years, the emissions control in nitrogen oxides (NO x ) was conducted across China, but how the concentrations of NO x and its product ozone (O3) in the atmosphere varied in space and time remains uncertain. Here, the spatial and temporal distributions of nitrogen dioxide (NO2) and O3 in 348 cities of China based on the hourly concentrations data during 2015–2020 were investigated, and the relationships among NO2, O3 and meteorological and socioeconomic parameters were explored. It is shown that higher NO2 and O3 concentrations were mainly distributed in North, East and Central China, which are economically developed and densely populated regions. The annual mean concentrations of NO2 increased from 2015 to 2017 but decreased from 2017 to 2020. The annual variations in O3 generally exhibited an upward trend in 2015–2019 but decreased by 5% from 2019 to 2020. About 74% and 78% of cities had a decline in NO2 and O3 in 2020, respectively, compared to 2019, due to the limits of the motorized transports and industrial production activities during COVID-19 lockdown. The monthly mean concentrations of NO2 showed an unusual decrease in February in all regions due to the reduced emissions during the Chinese Spring Festival holidays. Compared to 2019, the mean concentrations of NO2 in January, February and March, 2020 during COVID-19 lockdown decreased by 16%, 28% and 20%, respectively; O3 increased by 13% and 14% in January and February, respectively, but decreased by 2% in March, 2020. NO2 and O3 concentrations are likely associated with anthropogenic and natural emissions. In addition, meteorological parameters can affect NO2 and O3 concentrations by influencing the production process, the diffusion and local accumulation, and the regional circulations.

Impact of 3D radiative transfer on airborne NO<sub>2</sub> imaging remote sensing over cities with buildings

Abstract. Airborne imaging remote sensing is increasingly used to map the spatial distribution of nitrogen dioxide (NO2) in cities. Despite the small ground-pixel size of the sensors, the measured NO2 distributions are much smoother than one would expect from high-resolution model simulations of NO2 over cities. This could partly be caused by 3D radiative transfer effects due to observation geometry, adjacency effects and effects of buildings. Here, we present a case study of imaging a synthetic NO2 distribution for a district of Zurich using the 3D MYSTIC (Monte carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres) solver of the libRadtran radiative transfer library. We computed NO2 slant column densities (SCDs) using the recently implemented 3D-box air mass factors (3D-box AMFs) and a new urban canopy module to account for the effects of buildings. We found that for a single ground pixel (50 m × 50 m) more than 50 % of the sensitivity is located outside of the pixel, primarily in the direction of the main optical path between sun, ground pixel, and instrument. Consequently, NO2 SCDs are spatially smoothed, which results in an increase over roads when they are parallel to the optical path and a decrease otherwise. When buildings are included, NO2 SCDs are reduced on average by 5 % due to the reduced sensitivity to NO2 in the shadows of the buildings. The effects of buildings also introduce a complex pattern of variability in SCDs that would show up in airborne observations as an additional noise component (about 12 µmol m−2) similar to the magnitude of typical measurement uncertainties. The smearing of the SCDs cannot be corrected using 1D-layer AMFs that assume horizontal homogeneity and thus remains in the final NO2 map. The 3D radiative transfer effects by including buildings need to be considered to compute more accurate AMFs and to reduce biases in NO2 vertical columns obtained from high-resolution city-scale NO2 remote sensing.

Investigating the Sources of Formaldehyde and Corresponding Photochemical Indications at a Suburb Site in Shanghai From MAX‐DOAS Measurements

AbstractMulti‐AXis‐Differential Optical Absorption Spectroscopy (MAX‐DOAS) measurements were performed to obtain vertical distributions of nitrogen dioxide (NO2), formaldehyde (HCHO) and glyoxal (CHOCHO) from October 17 to November 21, 2019 in Qingpu, a suburb of Shanghai, China. The consistencies of MAX‐DOAS and other independent observations of HCHO and NO2 verified the effectiveness of the inversion results. The secondary formation was identified as the predominant HCHO sources (57.4% of all) by a statistical regression analysis of HCHO with carbon monoxide (CO) and CHOCHO. The photochemical indications of HCHO were investigated through the ratios of HCHO to CHOCHO (RGF) and NO2 (RFN). The results showed that anthropogenic influence on RGF mainly existed in the layer of 0–800 m. The dependences of anthropogenic emission on RGF were negative in 0–200 m layer and positive in 200–800 m layer when NO2 level was higher than 5 ppbv. The sensitivity analysis of O3 formation in 0–2 km layer was concluded as follows: between 9:00 and 11:00 LT, the O3 formation was controlled by VOC‐limited regime alone; while it turned into transition regime in 1.7–2 km layer from 11:00 to 13:00 LT; then the transition regime moved down to the layer of 1.3–1.4 km from 13:00 to 15:00 LT and the NOx‐limited regime began to occur in 1.4–2.0 km layer.

Spatial land use regression of nitrogen dioxide over a 5-year interval in Calgary, Canada

ABSTRACTWe analysed the spatial distribution of nitrogen dioxide over Calgary (Canada) in summer 2010 and winter 2011 and in summer 2015 and winter 2016, and estimated land use regressions for 2015–16 (2010–11 models were estimated previously). As nitrogen dioxide exhibited spatial clustering, we evaluated the following spatial specifications against a linear model: spatially autoregressive (lag), spatially autoregressive (error), and geographically weighted regression. The spatially autoregressive (lag) specification performed best, achieving goodness-of-fit aligned with or greater than values reported in the literature. We compared the 2015–16 spatially autoregressive models with the 2010–11 models and reparametrized them on the 2010–11 and the 2015–16 data. Finally, we identified a single set of predictors to best fit the data. Nitrogen dioxide concentration decreased over the 5 years, retaining consistent spatial and seasonal patterns, with higher concentrations over traffic corridors and industrial areas, and greater variation in summer than winter. The multi-temporal analysis suggested that spatial land use regressions were robust over the time interval, despite moderate land use change. Multi-temporal spatial land use regressions yielded consistent predictors for each season over time, which can aid estimation of air pollution at fine spatial resolution over an extended time period.

THE IMPACT OF TRAFFIC DYNAMIC ON AIR QUALITY AND VULNERABLE OBJECTS NEAR BUSY STREET IN NOVI SAD

In this paper, the distribution of nitrogen dioxide originating from vehicles from a busy street in Novi Sad, located near residential buildings and kindergartens, was performed. Based on the calculated level of nitrogen dioxide emissions from the vehicle, as well as the most frequent meteorological conditions, simulation of nitrogen dioxide propagation was carried out using ADMS urban software. Considering that the street is a part of the bridge whose elevation is gradually increasing, this research reveals permanently and intense exposure of certain parts of the residential buildings. The results presented in this paper can serve as a useful basis for taking measures to reduce the impact of traffic on air quality and human health through urban planning particulary of vulnerable facilities.

Spatiotemporal distribution of nitrogen dioxide within and around a large-scale wind farm – a numerical case study

Abstract. As a renewable and clean energy source, wind power has become the most rapidly growing energy resource worldwide in the past decades. Wind power has been thought not to exert any negative impacts on the environment. However, since a wind farm can alter the local meteorological conditions and increase the surface roughness lengths, it may affect air pollutants passing through and over the wind farm after released from their sources and delivered to the wind farm. In the present study, we simulated the nitrogen dioxide (NO2) air concentration within and around the world's largest wind farm (Jiuquan wind farm in Gansu Province, China) using a coupled meteorology and atmospheric chemistry model WRF-Chem. The results revealed an edge effect, which featured higher NO2 levels at the immediate upwind and border region of the wind farm and lower NO2 concentration within the wind farm and the immediate downwind transition area of the wind farm. A surface roughness length scheme and a wind turbine drag force scheme were employed to parameterize the wind farm in this model investigation. Modeling results show that both parameterization schemes yield higher concentration in the immediate upstream of the wind farm and lower concentration within the wind farm compared to the case without the wind farm. We infer this edge effect and the spatial distribution of air pollutants to be the result of the internal boundary layer induced by the changes in wind speed and turbulence intensity driven by the rotation of the wind turbine rotor blades and the enhancement of surface roughness length over the wind farm. The step change in the roughness length from the smooth to rough surfaces (overshooting) in the upstream of the wind farm decelerates the atmospheric transport of air pollutants, leading to their accumulation. The rough to the smooth surface (undershooting) in the downstream of the wind farm accelerates the atmospheric transport of air pollutants, resulting in lower concentration level.

Ground-based MAX-DOAS observations of tropospheric aerosols, NO<sub>2</sub>, SO<sub>2</sub> and HCHO in Wuxi, China, from 2011 to 2014

Abstract. We characterize the temporal variation and vertical distribution of nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (HCHO) and aerosol extinction based on long-term multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations from May 2011 to November 2014 in Wuxi, China. A new inversion algorithm (PriAM) is implemented to retrieve profiles of the trace gases (TGs) and aerosol extinction (AE) from the UV spectra of scattered sunlight recorded by the MAX-DOAS instrument. We investigated two important aspects of the retrieval process. We found that the systematic seasonal variation of temperature and pressure (which is regularly observed in Wuxi) can lead to a systematic bias of the retrieved aerosol profiles (e.g. up to 20 % for the AOD) if it is not explicitly considered. In this study we take this effect into account for the first time. We also investigated in detail the reason for the differences of tropospheric vertical column densities derived from either the geometric approximation or by the integration of the retrieved profiles, which were reported by earlier studies. We found that these differences are almost entirely caused by the limitations of the geometric approximation (especially for high aerosol loads). The results retrieved from the MAX-DOAS observations are compared with independent techniques not only under cloud-free sky conditions, but also under various cloud scenarios. Under most cloudy conditions (except fog and optically thick clouds), the trace gas results still show good agreements. In contrast, for the aerosol results, only near-surface AE could be still well retrieved under cloudy situations. After applying a quality control procedure, the MAX-DOAS data are used to characterize the seasonal, diurnal and weekly variations of NO2, SO2, HCHO and aerosols. A regular seasonality of the three trace gases is found, but not for aerosols. Similar annual variations of the profiles of the trace gases appear in different years. Only NO2 shows a significant seasonality of the diurnal variations. Considerable amplitudes of weekly cycles occur for NO2 and SO2, but not for HCHO and aerosols. The TGs and aerosols show good correlations, especially for HCHO in winter. More pronounced wind direction dependencies, especially for the near-surface concentrations, are found for the trace gases than for the aerosols, which implies that the local emissions from nearby industrial areas (including traffic emissions) dominate the local pollution, while long-distance transport might also considerably contribute to the local aerosol levels.

Dispersion Modeling of Nitrogen Dioxide in Ambient Air of Ahvaz City

Background: Air emission modeling is used to study how pollutants are spread in the environment and to forecast their emissions rate in different climatic conditions in the study area. Today, air pollution threatens human public health and the environment, and Ahvaz is considered one of the main centers of air pollution in Iran. Objectives: This aim of this study was to evaluate the nitrogen dioxide distribution in Ahvaz by using the software program Screen 3. Materials and Methods: This analytical study was conducted in 2013 in Ahvaz. Data collections were performed by the Ahvaz Department of Environment. After processing the data received (averaging instruction set, coding), the file was converted to an input file for the software program Screen 3. In the next phase, study of the distribution and release of nitrogen dioxide during the year 2013 was modeled using the Screen 3 software. Results: Based on the results of this study, the highest and the lowest nitrogen dioxide concentration were in the Bureau of Meteorology and Head office of ADoE stations, respectively. The maximum concentration of NO 2 , at a height of 45 m, was 20 μg/m 3 .According to the findings of the modeling, examination of the combustion process was observed, and this was caused by the amount of nitrogen dioxide emitted by flue outlets, which was affected by temperature and pressure, fuel flow rates and inlet air temperature. Also, results showed that in most cases emission of air pollutants was towards the southwest. Conclusions: Results of this study showed that the Screen3 software is one of the popular models for estimating the distribution of air pollutants. Nitrogen dioxide emissions are highly regulated in most industrialized regions. Decreased emissions from fuel combustion, the use of scrubbers and filters that absorb pollution can be very useful for reduction and control of nitrogen dioxide emissions.

Assessing traffic and industrial contributions to ambient nitrogen dioxide and volatile organic compounds in a low pollution urban environment

Land use regression (LUR) modeling is an effective method for estimating fine-scale distributions of ambient air pollutants. The objectives of this study are to advance the methodology for use in urban environments with relatively low levels of industrial activity and provide exposure assessments for research on health effects of air pollution. Intraurban distributions of nitrogen dioxide (NO2) and the volatile organic compounds (VOCs) benzene, toluene and m- and p-xylene were characterized based on spatial monitoring and LUR modeling in Ottawa, Ontario, Canada. Passive samplers were deployed at 50 locations throughout Ottawa for two consecutive weeks in October 2008 and May 2009. Land use variables representing point, area and line sources were tested as predictors of pooled pollutant distributions. LUR models explained 96% of the spatial variability in NO2 and 75–79% of the variability in the VOC species. Proximity to highways, green space, industrial and residential land uses were significant in the final models. More notably, proximity to industrial point sources and road network intersections were significant predictors for all pollutants. The strong contribution of industrial point sources to VOC distributions in Ottawa suggests that facility emission data should be considered whenever possible. The study also suggests that proximity to road network intersections may be an effective proxy in areas where reliable traffic data are not available.