Current Status and Future Forecast of Short-lived Climate-Forced Ozone in Tehran, Iran, derived from Ground-Based and Satellite Observations

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO₂, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O₃ concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.

Application of a novel second-order differential equation grey model to forecast NOx emissions in China

Nitrogen oxide (NOx) contains two harmful air pollutants: nitric oxide (NO) and nitrogen dioxide (NO₂). The reasonable prediction of China's NOx emissions is of positive significance for the government to formulate environmental protection policies. To this end, a new grey prediction model with second-order differential equation is proposed in this paper, which has more reasonable model structure and better modeling performance than the traditional grey model. Secondly, according to the data characteristics of NOx emissions of China in recent years, a smoothing algorithm and weakening buffer operator are employed to process the original data to solve the rationality of the prediction results of the new model. Thirdly, the model for predicting China's NOx emissions has been constructed by the new proposed model. The results show that the mean comprehensive error of the new model is only 0.0692%, and its performance is much better than that of several other mainstream grey prediction models. Finally, the new model is applied to China's carbon dioxide prediction in the next 5 years, and the rationality of the prediction results is analyzed. Based on the prediction results, relevant countermeasures and suggestions are put forward.

Daily Satellite Observations of Nitrogen Dioxide Air Pollution Inequality in New York City, New York and Newark, New Jersey: Evaluation and Application

Urban air pollution disproportionately harms communities of color and low-income communities in the U.S. Intraurban nitrogen dioxide (NO₂) inequalities can be observed from space using the TROPOspheric Monitoring Instrument (TROPOMI). Past research has relied on time-averaged measurements, limiting our understanding of how neighborhood-level NO₂ inequalities co-vary with urban air quality and climate. Here, we use fine-scale (250 m × 250 m) airborne NO₂ remote sensing to demonstrate that daily TROPOMI observations resolve a major portion of census tract-scale NO₂ inequalities in the New York City-Newark urbanized area. Spatiotemporally coincident TROPOMI and airborne inequalities are well correlated (r = 0.82-0.97), with slopes of 0.82-1.05 for relative and 0.76-0.96 for absolute inequalities for different groups. We calculate daily TROPOMI NO₂ inequalities over May 2018-September 2021, reporting disparities of 25-38% with race, ethnicity, and/or household income. Mean daily inequalities agree with results based on TROPOMI measurements oversampled to 0.01° × 0.01° to within associated uncertainties. Individual and mean daily TROPOMI NO₂ inequalities are largely insensitive to pixel size, at least when pixels are smaller than ∼60 km², but are sensitive to low observational coverage. We statistically analyze daily NO₂ inequalities, presenting empirical evidence of the systematic overburdening of communities of color and low-income neighborhoods with polluting sources, regulatory ozone co-benefits, and worsened NO₂ inequalities and cumulative NO₂ and urban heat burdens with climate change.

Impacts of TROPOMI-Derived NO X Emissions on NO2 and O3 Simulations in the NCP during COVID-19

NO₂ and O₃ simulations have great uncertainties during the COVID-19 epidemic, but their biases and spatial distributions can be improved with NO₂ assimilations. This study adopted two top-down NO _X inversions and estimated their impacts on NO₂ and O₃ simulation for three periods: the normal operation period (P1), the epidemic lockdown period following the Spring Festival (P2), and back to work period (P3) in the North China Plain (NCP). Two TROPOspheric Monitoring Instrument (TROPOMI) NO₂ retrievals came from the Royal Netherlands Meteorological Institute (KNMI) and the University of Science and Technology of China (USTC), respectively. Compared to the prior NO _X emissions, the two TROPOMI posteriors greatly reduced the biases between simulations with in situ measurements (NO₂ MREs: prior 85%, KNMI -27%, USTC -15%; O₃ MREs: Prior -39%, KNMI 18%, USTC 11%). The NO _X budgets from the USTC posterior were 17-31% higher than those from the KNMI one. Consequently, surface NO₂ levels constrained by USTC-TROPOMI were 9-20% higher than those by the KNMI one, and O₃ is 6-12% lower. Moreover, USTC posterior simulations showed more significant changes in adjacent periods (surface NO₂: P2 vs P1, -46%, P3 vs P2, +25%; surface O₃: P2 vs P1, +75%, P3 vs P2, +18%) than the KNMI one. For the transport flux in Beijing (BJ), the O₃ flux differed by 5-6% between the two posteriori simulations, but the difference of NO₂ flux between P2 and P3 was significant, where the USTC posterior NO₂ flux was 1.5-2 times higher than the KNMI one. Overall, our results highlight the discrepancies in NO₂ and O₃ simulations constrained by two TROPOMI products and demonstrate that the USTC posterior has lower bias in the NCP during COVD-19.

Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion

Daily emission estimates are essential for tracking the dynamic changes in emission sources. In this work, we estimate daily emissions of coal-fired power plants in China during 2017-2020 by combining information from the unit-based China coal-fired Power plant Emissions Database (CPED) and real-time measurements from continuous emission monitoring systems (CEMS). We develop a step-by-step method to screen outliers and impute missing values for data from CEMS. Then, plant-level daily profiles of flue gas volume and emissions obtained from CEMS are coupled with annual emissions from CPED to derive daily emissions. Reasonable agreement is found between emission variations and available statistics (i.e., monthly power generation and daily coal consumption). Daily power emissions are in the range of 6267-12,994, 0.4-1.3, 6.5-12.0, and 2.5-6.8 Gg for CO₂, PM_2.5, NO _x , and SO₂, respectively, with high emissions in winter and summer caused by heating and cooling demand. Our estimates can capture sudden decreases (e.g., those associated with COVID-19 lockdowns and short-term emission controls) or increases (e.g., those related to a drought) in daily power emissions during typical socioeconomic events. We also find that weekly patterns from CEMS exhibit no obvious weekend effect compared to those in previous studies. The daily power emissions will help to improve chemical transport modeling and facilitate policy formulation.

High-Dispersed V2O5-CuOX Nanoparticles on h-BN in NH3-SCR and NH3-SCO Performance

Typically, to meet emission regulations, the selective catalytic reduction of NO_X with NH₃ (NH₃-SCR) technology cause NH₃ emissions owing to high NH₃/NO_X ratios to meet emission regulations. In this study, V-Cu/BN-Ti was used to remove residual NO_X and NH₃. Catalysts were evaluated for selective catalytic oxidation of NH₃ (NH₃-SCO) in the NH₃-SCR reaction at 200–300 °C. The addition of vanadium and copper increased the number of Brønsted and Lewis acid sites available for the reaction by increasing the ratio of V⁵⁺ and forming Cu⁺ species, respectively. Furthermore, h-BN was dispersed in the catalyst to improve the content of vanadium and copper species on the surface. NH₃ and NO_X conversion were 98% and 91% at 260 °C, respectively. Consequently, slipped NH₃ (NH₃-Slip) emitted only 2% of the injected ammonia. Under SO₂ conditions, based on the NH₃ oxidation reaction, catalytic deactivation was improved by addition of h-BN. This study suggests that h-BN is a potential catalyst that can help remove residual NO_X and meet NH₃ emission regulations when placed at the bottom of the SCR catalyst layer in coal-fired power plants.

Acidity of rainfall samples in close vicinity of coal-fired power plant with wet cooling tower with flue gas injection

The paper presents measurement data concerning the degree of acidification of precipitation collected during a 6-month measurement campaign carried out in an immediate vicinity of a power plant, where the cooling tower was used for discharging flue gases as a product of coal combustion. As reference, data obtained from parallel measurements carried out at a monitoring station considered as city background station were used. High acidity of precipitation was anticipated due to reactions of acid gases contained in the combustion gases with water, which already occur inside the cooling tower. The results have not confirmed this assumption. The pH value of the precipitation samples was significantly higher than the pH of rainwater at the background station located 18 km away from the power plant.

Interference from HONO in the measurement of ambient air NO2 via photolytic conversion and quantification of NO

The reference method to quantify mixing ratios of the criteria air pollutant nitrogen dioxide (NO₂) is NO-O₃ chemiluminescence (CL), in which mixing ratios of nitric oxide (NO) are measured by sampling ambient air directly, and mixing ratios of NO_x (= sum of NO and NO₂) are measured by converting NO₂ to NO using, for example, heated molybdenum catalyst or, more selectively, photolytic conversion (P-CL). In this work, the nitrous acid (HONO) interference in the measurement of NO₂ by P-CL was investigated. Results with two photolytic NO₂ converters are presented. The first used radiation centered at 395 nm, a wavelength region commonly utilized in P-CL. The second used light at 415 nm, where the overlap with the HONO absorption spectrum and hence its photolysis rate are less. Mixing ratios of NO₂, NO_x and HONO entering and exiting the converters were quantified by Thermal Dissociation Cavity Ring-down Spectroscopy (TD-CRDS). Both converters exhibited high NO₂ conversion efficiency (CF_NO2; > 90%) and partial conversion of HONO. Plots of CF against flow rate were consistent with photolysis frequencies of 4.2 s^-1 and 2.9 s^-1 for NO₂ and 0.25 s^-1 and 0.10 s^-1 for HONO at 395 nm and 415 nm, respectively. CF_HONO was larger than predicted from the overlap of the emission and HONO absorption spectra. The results imply that measurements of NO₂ by P-CL marginally but systematically overestimate true NO₂ concentrations, and that this interference should be considered in environments with high HONO:NO₂ ratios such as the marine boundary layer or in biomass burning plumes.

Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing

Global lockdowns in response to the COVID-19 pandemic have led to changes in the anthropogenic activities resulting in perceivable air quality improvements. Although several recent studies have analyzed these changes over different regions of the globe, these analyses have been constrained due to the usage of station based data which is mostly limited up to the metropolitan cities. Also the quantifiable changes have been reported only for the developed and developing regions leaving the poor economies (e.g. Africa) due to the shortage of in-situ data. Using a comprehensive set of high spatiotemporal resolution satellites and merged products of air pollutants, we analyze the air quality across the globe and quantify the improvement resulting from the suppressed anthropogenic activity during the lockdowns. In particular, we focus on megacities, capitals and cities with high standards of living to make the quantitative assessment. Our results offer valuable insights into the spatial distribution of changes in the air pollutants due to COVID-19 enforced lockdowns. Statistically significant reductions are observed over megacities with mean reduction by 19.74%, 7.38% and 49.9% in nitrogen dioxide (NO₂), aerosol optical depth (AOD) and PM_2.5 concentrations. Google Earth Engine empowered cloud computing based remote sensing is used and the results provide a testbed for climate sensitivity experiments and validation of chemistry-climate models. Additionally, Google Earth Engine based apps have been developed to visualize the changes in a real-time fashion.

Observing Nitrogen Dioxide Air Pollution Inequality Using High-Spatial-Resolution Remote Sensing Measurements in Houston, Texas

Houston, Texas is a major U.S. urban and industrial area where poor air quality is unevenly distributed and a disproportionate share is located in low-income, non-white, and Hispanic neighborhoods. We have traditionally lacked city-wide observations to fully describe these spatial heterogeneities in Houston and in cities globally, especially for reactive gases like nitrogen dioxide (NO₂). Here, we analyze novel high-spatial-resolution (250 m × 500 m) NO₂ vertical columns measured by the NASA GCAS airborne spectrometer as part of the September-2013 NASA DISCOVER-AQ mission and discuss differences in population-weighted NO₂ at the census-tract level. Based on the average of 35 repeated flight circuits, we find 37 ± 6% higher NO₂ for non-whites and Hispanics living in low-income tracts (LIN) compared to whites living in high-income tracts (HIW) and report NO₂ disparities separately by race ethnicity (11-32%) and poverty status (15-28%). We observe substantial time-of-day and day-to-day variability in LIN-HIW NO₂ differences (and in other metrics) driven by the greater prevalence of NO_x (≡NO + NO₂) emission sources in low-income, non-white, and Hispanic neighborhoods. We evaluate measurements from the recently launched satellite sensor TROPOMI (3.5 km × 7 km at nadir), averaged to 0.01° × 0.01° using physics-based oversampling, and demonstrate that TROPOMI resolves similar relative, but not absolute, tract-level differences compared to GCAS. We utilize the high-resolution FIVE and NEI NO_x inventories, plus one year of TROPOMI weekday-weekend variability, to attribute tract-level NO₂ disparities to industrial sources and heavy-duty diesel trucking. We show that GCAS and TROPOMI spatial patterns correspond to the surface patterns measured using aircraft profiling and surface monitors. We discuss opportunities for satellite remote sensing to inform decision making in cities generally.

Potential interferences in photolytic nitrogen dioxide converters for ambient air monitoring: Evaluation of a prototype

Mixing ratios of the criteria air contaminant nitrogen dioxide (NO₂) are commonly quantified by reduction to nitric oxide (NO) using a photolytic converter followed by NO-O₃ chemiluminescence (CL). In this work, the performance of a photolytic NO₂ converter prototype originally designed for continuous emission monitoring and emitting light at 395 nm was evaluated. Mixing ratios of NO₂ and NO_x (= NO + NO₂) entering and exiting the converter were monitored by blue diode laser cavity ring-down spectroscopy (CRDS). The NO₂ photolysis frequency was determined by measuring the rate of conversion to NO as a function of converter residence time and found to be 4.2 s^-1. A maximum 96% conversion of NO₂ to NO over a large dynamic range was achieved at a residence time of (1.5 ± 0.3) s, independent of relative humidity. Interferences from odd nitrogen (NO_y) species such as peroxyacyl nitrates (PAN; RC(O)O₂NO₂), alkyl nitrates (AN; RONO₂), nitrous acid (HONO), and nitric acid (HNO₃) were evaluated by operating the prototype converter outside its optimum operating range (i.e., at higher pressure and longer residence time) for easier quantification of interferences. Four mechanisms that generate artifacts and interferences were identified as follows: direct photolysis, foremost of HONO at a rate constant of 6% that of NO₂; thermal decomposition, primarily of PAN; surface promoted photochemistry; and secondary chemistry in the connecting tubing. These interferences are likely present to a certain degree in all photolytic converters currently in use but are rarely evaluated or reported. Recommendations for improved performance of photolytic converters include operating at lower cell pressure and higher flow rates, thermal management that ideally results in a match of photolysis cell temperature with ambient conditions, and minimization of connecting tubing length. When properly implemented, these interferences can be made negligibly small when measuring NO₂ in ambient air.

IMPLICATIONS

A new near-UV photolytic converter for measurement of the criteria pollutant nitrogen dioxide (NO2) in ambient air by NO-O3 chemiluminescence (CL) was characterized. Four mechanisms that generate interferences were identified and investigated experimentally: direct photolysis of nitrous acid, which occurred at a rate constant 6% that of NO2, thermal decomposition of PAN and N2O5, surface promoted chemistry involving nitric acid, and secondary chemistry involving NO in the tubing connecting the converter and CL analyzer. These interferences are predicted to occur in all NO2 P-CL systems but can be avoided by appropriate thermal management and operating at high flow rates.

Inferring Changes in Summertime Surface Ozone-NOx-VOC Chemistry over U.S. Urban Areas from Two Decades of Satellite and Ground-Based Observations

Urban ozone (O₃) formation can be limited by NO_x, VOCs, or both, complicating the design of effective O₃ abatement plans. A satellite-retrieved ratio of formaldehyde to NO₂ (HCHO/NO₂), developed from theory and modeling, has previously been used to indicate O₃ formation chemistry. Here, we connect this space-based indicator to spatiotemporal variations in O₃ recorded by on-the-ground monitors over major U.S. cities. High-O₃ events vary nonlinearly with OMI HCHO and NO₂, and the transition from VOC-limited to NO_x-limited O₃ formation regimes occurs at higher HCHO/NO₂ value (3 to 4) than previously determined from models, with slight intercity variations. To extend satellite records back to 1996, we develop an approach to harmonize observations from GOME and SCIAMACHY that accounts for differences in spatial resolution and overpass time. Two-decade (1996-2016) multisatellite HCHO/NO₂ captures the timing and location of the transition from VOC-limited to NO_x-limited O₃ production regimes in major U.S. cities, which aligns with the observed long-term changes in urban-rural gradient of O₃ and the reversal of O₃ weekend effect. Our findings suggest promise for applying space-based HCHO/NO₂ to interpret local O₃ chemistry, particularly with the new-generation satellite instruments that offer finer spatial and temporal resolution.

Atmospheric Research Over the Western North Atlantic Ocean Region and North American East Coast: A Review of Past Work and Challenges Ahead

Decades of atmospheric research have focused on the Western North Atlantic Ocean (WNAO) region because of its unique location that offers accessibility for airborne and ship measurements, gradients in important atmospheric parameters, and a range of meteorological regimes leading to diverse conditions that are poorly understood. This work reviews these scientific investigations for the WNAO region, including the East Coast of North America and the island of Bermuda. Over 50 field campaigns and long-term monitoring programs, in addition to 715 peer-reviewed publications between 1946 and 2019 have provided a firm foundation of knowledge for these areas. Of particular importance in this region has been extensive work at the island of Bermuda that is host to important time series records of oceanic and atmospheric variables. Our review categorizes WNAO atmospheric research into eight major categories, with some studies fitting into multiple categories (relative %): Aerosols (25%), Gases (24%), Development/Validation of Techniques, Models, and Retrievals (18%), Meteorology and Transport (9%), Air-Sea Interactions (8%), Clouds/Storms (8%), Atmospheric Deposition (7%), and Aerosol-Cloud Interactions (2%). Recommendations for future research are provided in the categories highlighted above.

Modeling Emissions and Ozone Air Quality Impacts of Future Scenarios for Energy and Power Production in the Rocky Mountain States

This study examines air quality impacts of scenarios for energy production and use in 2030 across Colorado, northern New Mexico, Utah, and Wyoming. Scenarios feature contrasting levels of oil and gas production and shares of electricity from coal, natural gas, and renewables. Hourly emissions are resolved for individual power plants; oil and gas emissions are basin-specific. Ozone decreased from 2011 to the 2030 baseline, with median and 90th percentile reductions in maximum daily 8 h average (MDA8) ozone across the four-state domain of 3.5 and 7.1 ppb, respectively, resulting in 200 fewer premature deaths annually. Relative to the 2030 baseline, MDA8 ozone increased in the "cheap gas" scenario, with median and 90th percentile increases of 0.1 and 1.0 ppb, and declined in a scenario with greenhouse gas (GHG) emissions fees, with median and 90th percentile reductions of 0.2 and 1.5 ppb. Reduced coal generation lowered SO₂ emissions in all future scenarios compared to 2011. GHG emissions from electricity and oil and gas production declined by 4% (CO₂-equivalent) from 2011 to the 2030 baseline, increased by 10% from the 2030 baseline to the cheap gas scenario, and declined by 28% from the 2030 baseline to the GHG fees scenario.

Health benefits and control costs of tightening particulate matter emissions standards for coal power plants - The case of Northeast Brazil

Exposure to ambient particulate matter (PM) caused an estimated 4.2 million deaths worldwide in 2015. However, PM emission standards for power plants vary widely. To explore if the current levels of these standards are sufficiently stringent in a simple cost-benefit framework, we compared the health benefits (avoided monetized health costs) with the control costs of tightening PM emission standards for coal-fired power plants in Northeast (NE) Brazil, where ambient PM concentrations are below World Health Organization (WHO) guidelines. We considered three Brazilian PM₁₀ (PM_x refers to PM with a diameter under x micrometers) emission standards and a stricter U.S. EPA standard for recent power plants. Our integrated methodology simulates hourly electricity grid dispatch from utility-scale power plants, disperses the resulting PM_2.5, and estimates selected human health impacts from PM_2.5 exposure using the latest integrated exposure-response model. Since the emissions inventories required to model secondary PM are not available in our study area, we modeled only primary PM so our benefit estimates are conservative. We found that tightening existing PM₁₀ emission standards yields health benefits that are over 60 times greater than emissions control costs in all the scenarios we considered. The monetary value of avoided hospital admissions alone is at least four times as large as the corresponding control costs. These results provide strong arguments for considering tightening PM emission standards for coal-fired power plants worldwide, including in regions that meet WHO guidelines and in developing countries.

Impact of Control Measures on Nitrogen Oxides, Sulfur Dioxide and Particulate Matter Emissions from Coal-Fired Power Plants in Anhui Province, China

Anhui is one of the highest provincial emitters of air pollutants in China due to its large coal consumption in coal-fired plants. In this study, the total emissions of nitrogen oxides (NOx), sulfur dioxide (SO2) and particulate matter (PM) from coal-fired power plants in Anhui were investigated to assess the impact of control measures on the atmospheric emissions based upon continuous emission monitoring systems (CEMS). The total NOx, SO2 and PM emissions significantly decreased from 2013 to 2017 and they were estimated at 24.5 kt, 14.8 kt and 3.0 kt in 2017, respectively. The emission reductions of approximately 79.0%, 70.1% and 81.2% were achieved in 2017 compared with a 2013 baseline, respectively, due to the application of high-efficiency emission control measures, including the desulfurization, denitration and dust-removing devices and selective catalytic reduction (SCR). The NOx, SO2 and PM emission intensities were 0.125 g kWh−1, 0.076 g kWh−1 and 0.015 g kWh−1 in 2017, respectively, which were lower than the average of national coal-fired units. The coal-fired units with ≥600 MW generated 80.6% of the total electricity amount while they were estimated to account for 70.5% of total NOx, 70.1% of total SO2 and 71.9% of total PM. Their seasonal emissions showed a significant correlation to the power generation with the maximum correlation found in summer (July and August) and winter (January and December). The major regional contributors are the cities along the Huai River Basin and Yangtze River Basin, such as Huainan, Huaibei, Tongling, Maanshan and Wuhu, and the highest emission occurred in Huainan, accounting for approximately 26–40% of total emission from all the power plants. Our results indicated that the application of desulfurization, denitration and dust-removing devices has played an important role in controlling air pollutant emissions from coal-fired power plants.

Performance Evaluation of CCAM-CTM Regional Airshed Modelling for the New South Wales Greater Metropolitan Region

A comprehensive evaluation of the performance of the coupled Conformal Cubic Atmospheric Model (CCAM) and Chemical Transport Model (CTM) (CCAM-CTM) for the New South Wales Greater Metropolitan Region (NSW GMR) was conducted based on modelling results for two periods coinciding with measurement campaigns undertaken during the Sydney Particle Study (SPS), namely the summer in 2011 (SPS1) and the autumn in 2012 (SPS2). The model performance was evaluated for fine particulate matter (PM2.5), ozone (O3) and nitrogen dioxide (NO2) against air quality data from the NSW Government’s air quality monitoring network, and PM2.5 components were compared with speciated PM measurements from the Sydney Particle Study’s Westmead sampling site. The model tends to overpredict PM2.5 with normalised mean bias (NMB) less than 20%, however, moderate underpredictions of the daily peak are found on high PM2.5 days. The PM2.5 predictions at all sites comply with performance criteria for mean fractional bias (MFB) of ±60%, but only PM2.5 predictions at Earlwood further comply with the performance goal for MFB of ±30% during both periods. The model generally captures the diurnal variations in ozone with a slight underestimation. The model also tends to underpredict daily maximum hourly ozone. Ozone predictions across regions in SPS1, as well as in Sydney East, Sydney Northwest and Illawarra regions in SPS2 comply with the benchmark of MFB of ±15%, however, none of the regions comply with the benchmark for mean fractional error (MFE) of 35%. The model reproduces the diurnal variations and magnitudes of NO2 well, with a slightly underestimating tendency across the regions. The MFE and normalised mean error (NME) for NO2 predictions fall well within the ranges inferred from other studies. Model results are within a factor of two of measured averages for sulphate, nitrate, sodium and organic matter, with elemental carbon, chloride, magnesium and ammonium being underpredicted. The overall performance of CCAM-CTM modelling system for the NSW GMR is comparable to similar model predictions by other regional airshed models documented in the literature. The performance of the modelling system is found to be variable according to benchmark criteria and depend on the location of the sites, as well as the time of the year. The benchmarking of CCAM-CTM modelling system supports the application of this model for air quality impact assessment and policy scenario modelling to inform air quality management in NSW.

Modeling Ozone in the Eastern U.S. using a Fuel-Based Mobile Source Emissions Inventory

Recent studies suggest overestimates in current U.S. emission inventories of nitrogen oxides (NO _x = NO + NO₂). Here, we expand a previously developed fuel-based inventory of motor-vehicle emissions (FIVE) to the continental U.S. for the year 2013, and evaluate our estimates of mobile source emissions with the U.S. Environmental Protection Agency's National Emissions Inventory (NEI) interpolated to 2013. We find that mobile source emissions of NO _x and carbon monoxide (CO) in the NEI are higher than FIVE by 28% and 90%, respectively. Using a chemical transport model, we model mobile source emissions from FIVE, and find consistent levels of urban NO _x and CO as measured during the Southeast Nexus (SENEX) Study in 2013. Lastly, we assess the sensitivity of ozone (O₃) over the Eastern U.S. to uncertainties in mobile source NO _x emissions and biogenic volatile organic compound (VOC) emissions. The ground-level O₃ is sensitive to reductions in mobile source NO _x emissions, most notably in the Southeastern U.S. and during O₃ exceedance events, under the revised standard proposed in 2015 (>70 ppb, 8 h maximum). This suggests that decreasing mobile source NO _x emissions could help in meeting more stringent O₃ standards in the future.

Unexpected slowdown of US pollutant emission reduction in the past decade

Emissions of nitrogen oxides (NO_x) have a large impact on air quality and climate change as precursors in the formation of ozone and secondary aerosols. We find that NO_x emissions have not been decreasing as expected in recent years (2011–2015) when comparing top-down estimates from satellites and surface NO₂ measurements to the trends predicted from the US Environmental Protection Agency’s emission inventory data. The discrepancy can be explained by the growing relative contribution of industrial, area, and off-road mobile sources of emissions, decreasing relative contribution of on-road gasoline vehicles, and slower than expected decreases in on-road diesel NO_x emissions, with implications for air-quality management.

Ground and satellite observations show that air pollution regulations in the United States (US) have resulted in substantial reductions in emissions and corresponding improvements in air quality over the last several decades. However, large uncertainties remain in evaluating how recent regulations affect different emission sectors and pollutant trends. Here we show a significant slowdown in decreasing US emissions of nitrogen oxides (NO_x) and carbon monoxide (CO) for 2011–2015 using satellite and surface measurements. This observed slowdown in emission reductions is significantly different from the trend expected using US Environmental Protection Agency (EPA) bottom-up inventories and impedes compliance with local and federal agency air-quality goals. We find that the difference between observations and EPA’s NO_x emission estimates could be explained by: (i) growing relative contributions of industrial, area, and off-road sources, (ii) decreasing relative contributions of on-road gasoline, and (iii) slower than expected decreases in on-road diesel emissions.

Southeast Atmosphere Studies: learning from model-observation syntheses

Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts.

Air pollution at Rochester, NY: Long-term trends and multivariate analysis of upwind SO2 source impacts

There have been many changes in the air pollutant sources in the northeastern United States since 2001. To assess the effect of these changes, trend analyses of the monthly average values were performed on PM_2.5 and its components including major ions, elemental carbon (EC), organic carbon (OC), and gaseous pollutant concentrations measured between 2001 (in some cases 1999) and 2015 at the NYS Department of Environmental Conservation sites in Rochester, NY. Mann-Kendall regression with Sen's slope was applied to estimate the trends and seasonality. Using piecewise regression, significant reductions in the air pollution of Rochester area were observed between 2008 and 2010 when a 260MW coal-fired power plant was decommissioned, new heavy-duty diesel trucks had to be equipped with catalytic regenerator traps, and the economic recession that began in 2008 reduced traffic and other activities. The monthly average PM_2.5 mass showed a downward trend (-5μg/m³; -41%) in Rochester between 2001 and 2015. This change is largely due to reductions in particulate sulfate that showed a 65% decrease. The sulfate concentrations were compared to changes in SO₂ emissions in seventeen upwind source domains, and other systematic changes by multivariate linear regression. Selectivity ratio obtained from target projection discriminated the most important source domains that are SO₂ emissions from Georgia for winter, North Carolina for transition (spring and fall) and Ohio along with other influences for summer. North Carolina and Michigan were identified as the main sources for entire period. These observations suggest that any further reductions in the specified regional SO₂ emissions would result in a proportional decrease in sulfate in Rochester.

Evaluating a Space-Based Indicator of Surface Ozone-NO x -VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends

Determining effective strategies for mitigating surface ozone (O₃) pollution requires knowledge of the relative ambient concentrations of its precursors, NO _x , and VOCs. The space-based tropospheric column ratio of formaldehyde to NO₂ (FNR) has been used as an indicator to identify NO _x -limited versus NO _x -saturated O₃ formation regimes. Quantitative use of this indicator ratio is subject to three major uncertainties: (1) the split between NO _x -limited and NO _x -saturated conditions may shift in space and time, (2) the ratio of the vertically integrated column may not represent the near-surface environment, and (3) satellite products contain errors. We use the GEOS-Chem global chemical transport model to evaluate the quantitative utility of FNR observed from the Ozone Monitoring Instrument over three northern midlatitude source regions. We find that FNR in the model surface layer is a robust predictor of the simulated near-surface O₃ production regime. Extending this surface-based predictor to a column-based FNR requires accounting for differences in the HCHO and NO₂ vertical profiles. We compare four combinations of two OMI HCHO and NO₂ retrievals with modeled FNR. The spatial and temporal correlations between the modeled and satellite-derived FNR vary with the choice of NO₂ product, while the mean offset depends on the choice of HCHO product. Space-based FNR indicates that the spring transition to NO _x -limited regimes has shifted at least a month earlier over major cities (e.g., New York, London, and Seoul) between 2005 and 2015. This increase in NO _x sensitivity implies that NO _x emission controls will improve O₃ air quality more now than it would have a decade ago.

The effects of forest canopy shading and turbulence on boundary layer ozone

The chemistry of the Earth's atmosphere close to the surface is known to be strongly influenced by vegetation. However, two critical aspects of the forest environment have been neglected in the description of the large-scale influence of forests on air pollution: the reduction of photolysis reaction rates and the modification of vertical transport due to the presence of foliage. Here we show that foliage shading and foliage-modified vertical diffusion have a profound influence on atmospheric chemistry, both at the Earth's surface and extending throughout the atmospheric boundary layer. The absence of these processes in three-dimensional models may account for 59–72% of the positive bias in North American surface ozone forecasts, and up to 97% of the bias in forested regions within the continent. These processes are shown to have similar or greater influence on surface ozone levels as climate change and current emissions policy scenario simulations.

Fully quantifying the influence of vegetation on atmospheric chemistry remains challenging. Here, the authors show that forest canopy shading and turbulence significantly modify air pollution throughout the atmospheric boundary layer, and must be taken into account in models of the atmosphere.

Long-term trends of Nitrogen oxides and surface ozone concentrations in Tehran city, 2002-2011

BACKGROUND AND AIM

Tropospheric ozone is a problem with multi aspects - hazard to human health, plant, and welfare and a key factor to climate change, air pollution and atmosphere chemistry, as well. Behavior of ozone and nitrogen oxides (NO, and NO2) concentration is highly complex and variable; therefore, their trends as short and long-term were significantly attended. Most of the studies were carried out on the behavior of pollutant concentrations in North America, Europe, and East Asia, but few studies have been conducted in west Asia. The aim of this study was to assess daily changes and long-term trend of ozone and nitrogen oxides concentrations in Tehran city, Iran from March 2002 to September 2011.

MATERIAL AND METHODS

Data were collected from 18 air quality monitoring stations. The data were sorted as daily mean of 10 years (daily changes) and annual mean for each year (long-term trend). One-sample test was used to assess the statistical significance.

RESULTS

Current findings indicated that changes of ozone, NO, and NO2 concentrations are dependent from job shifts and photochemical reactions. Annual mean concentrations of ozone and NO2 were gradually increased, long-term trend of ozone and NO2 concentration indicated. The correlation between long term trend of ozone and NO2 was significant (p < 0.05).

CONCLUSION

The controlling program was the most important factor in long-term concentration of ozone, and nitrogen oxides, but some problems and difficulties were accounted to perform controlling program.

Temporalization of peak electric generation particulate matter emissions during high energy demand days

Underprediction of peak ambient pollution by air quality models hinders development of effective strategies to protect health and welfare. The U.S. Environmental Protection Agency's community multiscale air quality (CMAQ) model routinely underpredicts peak ozone and fine particulate matter (PM2.5) concentrations. Temporal misallocation of electricity sector emissions contributes to this modeling deficiency. Hourly emissions are created for CMAQ by use of temporal profiles applied to annual emission totals unless a source is matched to a continuous emissions monitor (CEM) in the National Emissions Inventory (NEI). More than 53% of CEMs in the Pennsylvania-New Jersey-Maryland (PJM) electricity market and 45% nationally are unmatched in the 2008 NEI. For July 2006, a United States heat wave with high electricity demand, peak electric sector emissions, and elevated ambient PM2.5 mass, we match hourly emissions for 267 CEM/NEI pairs in PJM (approximately 49% and 12% of unmatched CEMs in PJM and nationwide) using state permits, electricity dispatch modeling and CEMs. Hourly emissions for individual facilities can differ up to 154% during the simulation when measurement data is used rather than default temporalization values. Maximum CMAQ PM2.5 mass, sulfate, and elemental carbon predictions increase up to 83%, 103%, and 310%, at the surface and 51%, 75%, and 38% aloft (800 mb), respectively.

Bay breeze climatology at two sites along the Chesapeake bay from 1986-2010: Implications for surface ozone

Hourly surface meteorological measurements were coupled with surface ozone (O₃) mixing ratio measurements at Hampton, Virginia and Baltimore, Maryland, two sites along the Chesapeake Bay in the Mid-Atlantic United States, to examine the behavior of surface O₃ during bay breeze events and quantify the impact of the bay breeze on local O₃ pollution. Analyses were performed for the months of May through September for the years 1986 to 2010. The years were split into three groups to account for increasingly stringent environmental regulations that reduced regional emissions of nitrogen oxides (NO_x): 1986-1994, 1995-2002, and 2003-2010. Each day in the 25-year record was marked either as a bay breeze day, a non-bay breeze day, or a rainy/cloudy day based on the meteorological data. Mean eight hour (8-h) averaged surface O₃ values during bay breeze events were 3 to 5 parts per billion by volume (ppbv) higher at Hampton and Baltimore than on non-bay breeze days in all year periods. Anomalies from mean surface O₃ were highest in the afternoon at both sites during bay breeze days in the 2003-2010 study period. In conjunction with an overall lowering of baseline O₃ after the 1995-2002 period, the percentage of total exceedances of the Environmental Protection Agency (EPA) 75 ppbv 8-h O₃ standard that occurred on bay breeze days increased at Hampton for 2003-2010, while remaining steady at Baltimore. These results suggest that bay breeze circulations are becoming more important to causing exceedance events at particular sites in the region, and support the hypothesis of Martins et al. (2012) that highly localized meteorology increasingly drives air quality events at Hampton.

Removal dynamics of nitric oxide (NO) pollutant gas by pulse-discharged plasma technique

Nonthermal plasma technique has drawn extensive attentions for removal of air pollutants such as NO_x and SO₂. The NO removal mechanism in pulse discharged plasma is discussed in this paper. Emission spectra diagnosis indicates that the higher the discharge voltage is, the more the NO are removed and transformed into O, N, N₂, NO₂, and so forth. Plasma electron temperature T_e is ranged from 6400 K at 2.4 kV discharge voltage to 9500 K at 4.8 kV. After establishing a zero-dimensional chemical reaction kinetic model, the major reaction paths are clarified as the electron collision dissociation of NO into N and O during discharge and followed by single substitution of N on NO to form N₂ during and after discharge, compared with the small fraction of NO₂ formed by oxidizing NO. The reaction directions can be adjusted by N₂ additive, and the optimal N₂/NO mixing ratio is 2 : 1. Such a ratio not only compensates the disadvantage of electron competitive consumption by the mixed N₂, but also heightens the total NO removal extent through accelerating the NO oxidization process.

Considering the air quality impacts of bioenergy crop production: a case study involving Arundo donax

The expanding production of bioenergy crops may impact regional air quality through the production of volatile organic compounds such as isoprene. To investigate the effects of isoprene-emitting crops on air quality, specifically ozone (O(3)) and secondary organic aerosol (SOA) formation, we performed a series of model runs using the Weather Research and Forecasting model with Chemistry (WRF/Chem) coupled with the Model of Emissions of Gases and Aerosols from Nature (MEGAN) simulating a proposed cropland conversion to the giant cane Arundo donax for biomass production. Cultivation of A. donax in the relatively clean air of northeastern Oregon resulted in an average increase in 8 h O(3) levels of 0.52 ppb, while SOA was largely unaffected (<+0.01 μg m(-3)). Conversions in U.S. regions with reduced air quality (eastern Texas and northern Illinois) resulted in average 8 h O(3) increases of 2.46 and 3.97 ppb, respectively, with daily increases up to 15 ppb in the Illinois case, and daytime SOA increases up to 0.57 μg m(-3). While cultivation of isoprene-emitting bioenergy crops may be appropriate at some scales and in some regions, other areas may experience increased O(3) and SOA, highlighting the need to consider isoprene emissions when evaluating potential regional impacts of bioenergy crop production.

Flexible NO(x) abatement from power plants in the eastern United States

Emission controls that provide incentives for maximizing reductions in emissions of ozone precursors on days when ozone concentrations are highest have the potential to be cost-effective ozone management strategies. Conventional prescriptive emissions controls or cap-and-trade programs consider all emissions similarly regardless of when they occur, despite the fact that contributions to ozone formation may vary. In contrast, a time-differentiated approach targets emissions reductions on forecasted high ozone days without imposition of additional costs on lower ozone days. This work examines simulations of such dynamic air quality management strategies for NO(x) emissions from electric generating units. Results from a model of day-specific NO(x) pricing applied to the Pennsylvania-New Jersey-Maryland (PJM) portion of the northeastern U.S. electrical grid demonstrate (i) that sufficient flexibility in electricity generation is available to allow power production to be switched from high to low NO(x) emitting facilities, (ii) that the emission price required to induce EGUs to change their strategies for power generation are competitive with other control costs, (iii) that dispatching strategies, which can change the spatial and temporal distribution of emissions, lead to ozone concentration reductions comparable to other control technologies, and (iv) that air quality forecasting is sufficiently accurate to allow EGUs to adapt their power generation strategies.

Influence of coal-based thermal power plants on the spatial-temporal variability of tropospheric NO2 column over India

The oxides of nitrogen--NO(x) (NO and NO(2))--are an important constituent of the troposphere. The availability of relatively higher spatial (0.25° grid) and temporal (daily) resolution data from ozone monitoring instrument (OMI) onboard Aura helps us to better differentiate between the point sources such as thermal power plants from large cities and rural areas compared to previous sensors. The annual and seasonal (summer and winter) distributions shows very high mean tropospheric NO(2) in specific pockets over India especially over the Indo-Gangetic plains (up to 14.2 × 10(15) molecules/cm(2)). These pockets correspond with the known locations of major thermal power plants. The tropospheric NO(2) over India show a large seasonal variability that is also observed in the ground NO(2) data. The multiple regression analysis show that the influence of a unit of power plant (in gigawatts) over tropospheric NO(2) (×10(15) molecules/cm(2)) is around ten times compared to a unit of population (in millions) over India. The OMI data show that the NO(2) increases by 0.794 ± 0.12 (×10(15) molecules/cm(2); annual) per GW compared to a previous estimate of 0.014 (×10(15) molecules/cm(2)) over India. The increase of tropospheric NO(2) per gigawatt is found to be 1.088 ± 0.18, 0.898 ± 0.14, and 0.395 ± 0.13 (×10(15) molecules/cm(2)) during winter, summer, and monsoon seasons, respectively. The strong seasonal variation is attributed to the enhancement or suppression of NO(2) due to various controlling factors which is discussed here. The recent increasing trend (2005-2007) over rural thermal power plants pockets like Agori and Korba is due to recent large capacity additions in these regions.

Efficient characterization of pollutant-emission response under parametric uncertainty

An essential requirement of modeling for air quality management is to accurately simulate the responses of pollutant concentrations to changes in emissions. Uncertain model input parameters such as emission rates and reaction rate constants lead to uncertainty in model responses. However, traditional methods for characterizing parametric uncertainty are exceedingly computationally intensive. This paper presents methods for using high-order sensitivity coefficients in analytical equations to efficiently represent how the responsiveness of pollutants to emission reductions in the underlying photochemical model varies with simultaneous perturbations in multiple model input parameters. Separate approaches are introduced for characterizing the parametric uncertainty of pollutant response to a fixed or a variable amount of emission reduction. The approaches are demonstrated for an air pollution episode used in recent attainment planning in Georgia. For hypothetical scenarios in which domain-wide emission rates and photolysis rates are perturbed simultaneously by 50%, the reduced form models yield highly accurate predictions of the ozone impacts due to 50% reductions in nitrogen-oxide emissions in Atlanta (normalized mean bias 6.0%, normalized mean error 9.7%, R2=0.992) and inorganic particulate responses to Atlanta sulfur-dioxide emissions (-2.9% bias, 3.7% error, R2=1.000). Similar accuracy is achieved for pollutant responses to power plant emission controls.

A FRAMEWORK FOR EVALUATING REGIONAL-SCALE NUMERICAL PHOTOCHEMICAL MODELING SYSTEMS

This paper discusses the need for critically evaluating regional-scale (~200-2000 km) three-dimensional numerical photochemical air quality modeling systems to establish a model's credibility in simulating the spatio-temporal features embedded in the observations. Because of limitations of currently used approaches for evaluating regional air quality models, a framework for model evaluation is introduced here for determining the suitability of a modeling system for a given application, distinguishing the performance between different models through confidence-testing of model results, guiding model development, and analyzing the impacts of regulatory policy options. The framework identifies operational, diagnostic, dynamic, and probabilistic types of model evaluation. Operational evaluation techniques include statistical and graphical analyses aimed at determining whether model estimates are in agreement with the observations in an overall sense. Diagnostic evaluation focuses on process-oriented analyses to determine whether the individual processes and components of the model system are working correctly, both independently and in combination. Dynamic evaluation assesses the ability of the air quality model to simulate changes in air quality stemming from changes in source emissions and/or meteorology, the principal forces that drive the air quality model. Probabilistic evaluation attempts to assess the confidence that can be placed in model predictions using techniques such as ensemble modeling and Bayesian model averaging. The advantages of these types of model evaluation approaches are discussed in this paper.