Air Quality Planning Research Articles

Winter particulate pollution severity in North China driven by atmospheric teleconnections

Elevated levels of particulate matter in the atmosphere are hazardous to human health and the environment. Severe particulate pollution days, with daily mean PM2.5 concentrations exceeding 150 μg m−3, occurred frequently in North China, especially during the boreal winters of 2013–2019. Severe particulate pollution generally occurs under conducive weather patterns characterized by a stable atmosphere with weak winds, under which air pollutants emitted at the surface by human activities would accumulate. The occurrence of conducive weather patterns has been attributed to variations in numerous climate factors such as Arctic sea-ice cover, sea surface temperature and atmospheric teleconnections, but the dominant climate drivers remain unclear. Here, we show that the East Atlantic–West Russia teleconnection pattern and the Victoria mode of sea surface temperature anomalies are the top two dominant climate drivers that lead to conducive weather patterns in North China through the zonal and meridional propagations of Rossby waves. Our results suggest that, with the help of seasonal forecast from climate models, indices of these two drivers can be used to predict severe particulate pollution over North China for the coming winter, enabling us to protect human health by air-quality planning.

Impacts of aerosol–photolysis interaction and aerosol–radiation feedback on surface-layer ozone in North China during multi-pollutant air pollution episodes

Abstract. We examined the impacts of aerosol–radiation interactions, including the effects of aerosol–photolysis interaction (API) and aerosol–radiation feedback (ARF), on surface-layer ozone (O3) concentrations during four multi-pollutant air pollution episodes characterized by high O3 and PM2.5 levels during 28 July to 3 August 2014 (Episode1), 8–13 July 2015 (Episode2), 5–11 June 2016 (Episode3), and 28 June to 3 July 2017 (Episode4) in North China, by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model embedded with an integrated process analysis scheme. Our results show that API and ARF reduced the daytime shortwave radiative fluxes at the surface by 92.4–102.9 W m−2 and increased daytime shortwave radiative fluxes in the atmosphere by 72.8–85.2 W m−2, as the values were averaged over the complex air pollution areas (CAPAs) in each of the four episodes. As a result, the stabilized atmosphere decreased the daytime planetary boundary layer height and 10 m wind speed by 129.0–249.0 m and 0.05–0.15 m s−1, respectively, in CAPAs in the four episodes. Aerosols were simulated to reduce the daytime near-surface photolysis rates of J[NO2] and J[O1D] by 1.8 × 10−3–2.0 × 10−3 and 5.7 × 10−6–6.4 × 10−6 s−1, respectively, in CAPAs in the four episodes. All of the four episodes show the same conclusion, which is that the reduction in O3 by API is larger than that by ARF. API (ARF) was simulated to change daytime surface-layer O3 concentrations by −8.5 ppb (parts per billion; −2.9 ppb), −10.3 ppb (−1.0 ppb), −9.1 ppb (−0.9 ppb), and −11.4 ppb (+0.7 ppb) in CAPAs of the four episodes, respectively. Process analysis indicated that the weakened O3 chemical production made the greatest contribution to API effect, while the reduced vertical mixing was the key process for ARF effect. Our conclusions suggest that future PM2.5 reductions may lead to O3 increases due to the weakened aerosol–radiation interactions, which should be considered in air quality planning.

Characterization factors and other air quality impact metrics: Case study for PM2.5-emitting area sources from biofuel feedstock supply

In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM2.5) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.

Seasonal variations, temperature dependence, and sources of size-resolved PM components in Nanjing, east China

Size-segregated ambient particulate matter (PM) samples were collected seasonally in suburban Nanjing of east China from 2016 to 2017 and chemically speciated. In both fine (< 2.1 µm, PM2.1) and coarse (> 2.1 µm, PM>2.1) PM, organic carbon (OC) accounted for the highest fractions (26.9% ± 10.9% and 23.1% ± 9.35%) of all measured species, and NO3− lead in average concentrations of water-soluble inorganic ions (WSIIs). The size distributions of measured components were parameterized using geometric mean diameter (GMD). GMD values of NO3−, Cl−, OC, and PM for the whole size range varied from < 2.1 µm in winter to > 2.1 μm in warm seasons, which was due to the fact that the size distributions of semi-volatile components (e.g., NH4NO3, NH4Cl, and OC) had a dependency on the ambient temperature. Unlike OC, elemental carbon (EC), and elements, NH4+, NO3−, and SO42− exhibited an increase trend in GMD values with relative humidity, indicating that the hygroscopic growth might also play a role in driving seasonal changes of PM size distributions. Positive matrix factorization was performed using compositional data of fine and coarse particles, respectively. The secondary formation of inorganic salts contributing to the majority (> 70%) of fine PM and 20.2% ± 19.9% of speciated coarse PM. The remaining coarse PM content was attributed to a variety of dust sources. Considering that coarse and fine PM had comparable mass concentrations, more attention should be paid to local dust emissions in future air quality plans.

“Effective” Air Quality Plan and Constitutional Guarantees of Ecological Security

“Effective” Air Quality Plan and Constitutional Guarantees of Ecological Security

Impacts of biogenic emissions from urban landscapes on summer ozone and secondary organic aerosol formation in megacities

The impact of biogenic emissions on ozone and secondary organic aerosol (SOA) has been widely acknowledged; nevertheless, biogenic emissions emitted from urban landscapes have been largely ignored. We find that including urban isoprene in megacities like Beijing improves not only the modeled isoprene concentrations but also its diurnal cycle. Specifically, the mean bias of the simulated isoprene concentrations is reduced from 87% to 39% by adding urban isoprene emissions while keeping the diurnal cycle the same as that in non-urban or rural areas. Further adjusting the diurnal cycle of isoprene emissions to the urban profile steers the original early morning peak of the isoprene concentration to a double quasi-peak, i.e., bell shape, consistent with observations. The efficiency of ozone generation caused by isoprene emissions in urban Beijing is found to be twice as large as those in rural areas, indicative of vital roles of urban BVOC emissions in modulating the ozone formation. Our study also shows that in the future along with NOx emission reduction, isoprene emissions from urban landscapes will become more important for the formation of ozone in urban area, and their contributions may exceed that of isoprene caused by transport from rural areas. Finally, the impact of biogenic emissions on SOA is examined, revealing that biogenic induced SOA accounts for 16% of the total SOA in urban Beijing. The effect of isoprene on SOA (iSOA) is modulated through two pathways associated with the abundance of NOx emissions, and the effect can be amplified in future when NOx emissions are reduced. The findings of our study are not limited to Beijing but also apply to other megacities or densely populated regions, suggesting an urgent need to construct an accurate emission inventory for urban landscapes and evaluate their impact on ozone and SOA in air quality planning and management.

Probabilistic total PM2.5 emissions from vehicular sources in Australian perspective.

Motor vehicles operating on the road are a significant source of Particulate Matter (PM) emissions depending on the fuels used in the vehicles. Gasoline and Diesel vehicles are directly responsible for the tailpipe PM emissions (specifically PM2.5: particles ≤ 2.5µm), known as primary PM2.5 emissions. The other major direct emissions from the vehicles, which include volatile organic compounds (VOCs), and nitrogen oxides (NOx) contribute to the formation of secondary organic PM, also known as secondary organic aerosols (SOA), through some inter-related chemical reactions. The SOAs are highly toxic and contribute to a portion of total PM emissions. In this research, emission scenarios of both primary PM2.5 and SOA for a car-dependent expanding Australian city (Adelaide) were analyzed. The variability of traffic characteristics on road was considered and conducted a probabilistic emissions inventory for tailpipe primary PM2.5 and precursors, while statistical analysis of the probable chemical conversion ratios was considered for the SOA inventory. It was found that the tailpipe emissions from the vehicles were higher than the air quality standard, while the SOA contribution from the vehicles was not significantly high but contributed to the increase of total PM concentration. The analysis of the chemical transformation of SOA precursors justified the importance of conducting more detailed emissions modelling for sustainable urban air quality planning.

Non-linear response of PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; to changes in NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt; and NH&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; emissions in the Po basin (Italy): consequences for air quality plans

Abstract. Air pollution is one of the main causes of damages to human health in Europe, with an estimate of about 380 000 premature deaths per year in the EU28, as the result of exposure to fine particulate matter (PM2.5) only. In this work, we focus on one specific region in Europe, the Po basin, a region where chemical regimes are the most complex, showing important non-linear processes, especially those related to interactions between NOx and NH3. We analyse the sensitivity of PM2.5 concentration to NOx and NH3 emissions by means of a set of EMEP model simulations performed with different levels of emission reductions, from 25 % up to a total switch-off of those emissions. Both single and combined precursor reduction scenarios are applied to determine the most efficient emission reduction strategies and quantify the interactions between NOx and NH3 emission reductions. The results confirmed the peculiarity of secondary PM2.5 formation in the Po basin, characterised by contrasting chemical regimes within distances of a few (hundred) kilometres, as well as non-linear responses to emission reductions during wintertime. One of the striking results is the slight increase in the PM2.5 concentration levels when NOx emission reductions are applied in NOx-rich areas, such as the surroundings of Bergamo. The increased oxidative capacity of the atmosphere is the cause of the increase in PM2.5 induced by a reduction in NOx emission. This process could have contributed to the absence of a significant PM2.5 concentration decrease during the COVID-19 lockdowns in many European cities. It is important to account for this process when designing air quality plans, since it could well lead to transitionary increases in PM2.5 at some locations in winter as NOx emission reduction measures are gradually implemented. While PM2.5 chemical regimes, determined by the relative importance of the NOx vs. NH3 responses to emission reductions, show large variations seasonally and spatially, they are not very sensitive to moderate (up to 50 %–60 %) emission reductions. Beyond 25 % emission reduction strength, responses of PM2.5 concentrations to NOx emission reductions become non-linear in certain areas of the Po basin mainly during wintertime.

Study of Air Pollutions Caused by Exhaust Gases Emitted from Gasoline Vehicles in Erbil City

The environmental pollution caused by exhaust gas during parking in Erbil City’s Periodic Vehicle Inspection (PVI) was studied. The purpose of this research is to measure vehicle exhaust emissions from Erbil (PVI) gasoline vehicles. Tested the carbon monoxide (CO), carbon dioxide (CO2), oxygen (O2) and hydrocarbon (HC) emissions of gasoline vehicles. The statistical results show that there are significant differences between emission rates based on vehicle characteristics (such as fuel type, model year, engine fuel supply system, and regular maintenance). In addition, these factors will also affect the emission level test results based on the Iraqi vehicle emission standards. In addition, the emissions of these exhaust gases also depend on factors such as engine combustion design and operating conditions, fuel grade, lubricants, local road conditions and other factors, and continue to affect the concentration of pollutants emitted by vehicles (such as carbon dioxide and unburned hydrocarbons) These pollutants are very toxic to the human body and cause environmental pollution. Accurate estimates of pollutant emissions are needed to ensure proper design and implementation of air quality plans. Emission factors (EFs) are empirical functions between pollutant emissions and their activities. In this review article, in connection with the development of EFs found in emission models used to generate emission inventories, techniques for measuring emissions from road vehicles are studied. The emission measurement technologies covered include those most widely used for road vehicle emission data collection, namely chassis and engine dynamometer measurements, remote sensing, road tunnel research, and portable emission measurement systems (PEMS). The main advantages and disadvantages of each method with regard to emission modeling are presented. It also reviews ways in which EF can be derived from test data, with a clear distinction between data obtained under controlled conditions (engine and chassis dynamometer measurements using standard driving cycles) and measurements under real conditions.

Quantifying spatial heterogeneity of vulnerability to short-term PM2.5 exposure with data fusion framework

The current estimations of the burden of disease (BD) of PM2.5 exposure is still potentially biased by two factors: ignorance of heterogeneous vulnerabilities at diverse urbanization levels and reliance on the risk estimates from existing literature, usually from different locations. Our objectives are (1) to build up a data fusion framework to estimate the burden of PM2.5 exposure while evaluating local risks simultaneously and (2) to quantify their spatial heterogeneity, relationship to land-use characteristics, and derived uncertainties when calculating the disease burdens. The feature of this study is applying six local databases to extract PM2.5 exposure risk and the BD information, including the risks of death, cardiovascular disease (CVD), and respiratory disease (RD), and their spatial heterogeneities through our data fusion framework. We applied the developed framework to Tainan City in Taiwan as a use case estimated the risks by using 2006–2016 emergency department visit data, air quality monitoring data, and land-use characteristics and further estimated the BD caused by daily PM2.5 exposure in 2013. Our results found that the risks of CVD and RD in highly urbanized areas and death in rural areas could reach 1.20–1.57 times higher than average. Furthermore, we performed a sensitivity analysis to assess the uncertainty of BD estimations from utilizing different data sources, and the results showed that the uncertainty of the BD estimations could be contributed by different PM2.5 exposure data (20–32%) and risk values (0–86%), especially for highly urbanized areas. In conclusion, our approach for estimating BD based on local databases has the potential to be generalized to the developing and overpopulated countries and to support local air quality and health management plans.

Effects of vertical turbulent diffusivity on regional PM2.5 and O3 source contributions

In this study, we examined the effects of vertical turbulent diffusion on the absolute and relative strengths of foreign and domestic source contributions. We chose the Northeast Asia region because of the important transboundary air pollution issues requiring a sound understanding of source-receptor relationships in the region. We performed photochemical grid modeling by setting two different minimum vertical turbulent diffusivities (Kvmin): 1.0 m2/s for all areas (Run A); and 0.1 m2/s for non-urban areas and 1.0 m2/s for urban areas (Run B). Changes in modeled regional contributions to a downwind receptor in the Seoul Metropolitan Area (SMA), South Korea, were investigated from ground-level level to high altitudes for days representing local influence and transboundary transport dominant conditions. For the local influence day, Run A and Run B estimated daily average PM2.5 concentrations of 43.1 μg/m3 and 49.9 μg/m3 (domestic contributions of 16.7 μg/m3 and 19.7 μg/m3) and daily maximum 1-hour ozone values of 56.1 ppb and 56.4 ppb (domestic contributions of 9.1 ppb and 9.9 ppb) in the SMA at ground-level. For the transboundary transport dominant day, Run A and Run B estimated daily average PM2.5 concentrations of 72.0 μg/m3 and 65.9 μg/m3 (foreign contributions of 53.8 μg/m3 and 46.8 μg/m3) and daily maximum 1-hour ozone values of 78.7 ppb and 78.0 ppb (the largest foreign contributions of 27.2 ppb and 25.3 ppb from the Yangtze River Delta area according to both models) in the SMA. We observed that South Korea’s domestic contributions by Run B showed smaller PM2.5 differences (0.6 μg/m3; 10% relative to total PM2.5) than Run A (1.5 μg/m3; 14% relative to total PM2.5) between the local influence day and the transboundary transport dominant day. For ozone, Run A and Run B showed relatively consistent bulk concentrations and domestic contributions for both days but regional contributions of individual foreign source regions varied vertically. A total relative contribution by Chinese source regions ranged from 12–72% at or below the 15th vertical layer. When domestic sources had a strong influence, the relative ratios of foreign to domestic contributions were 1.6 and 1.5 according to Run A and Run B, respectively. However, when foreign sources had a strong influence, the relative ratios of foreign to domestic PM2.5 contributions were quite different (3.0 by Run A and 2.4 by Run B). These results suggest that when developing air quality plans in the Northeast Asian region, two sets of modeling with high and low Kvmin values are needed to account for domestic/foreign contributions in a more robust fashion.

Impacts of a climate change initiative on air pollutant emissions: Insights from the Covenant of Mayors

The Covenant of Mayors (CoM) is a successful European initiative which encourages local authorities to be proactive in fighting climate change. Recently, it expanded to cover adaptation and energy access/poverty and became a global initiative. In this study we investigate an additional perspective: synergies and trade-offs between climate and air quality. Signatories pledge to reduce their Greenhouse gas (GHG) emissions and voluntarily report their emissions, energy consumption and the measures that they carry out to reach their goals. We develop a methodology to estimate air pollutant emissions corresponding to CO2 emissions CoM signatories report, using information they already submit and national estimates of air pollutant emission factors. The methodology is applied to over 1600 signatories in Europe, representing over 80 million inhabitants. Results show that, in general, signatories are reducing both types of emissions. However, there are also cases where emissions increase. We explore the reasons behind these changes and highlight the role of technological improvement. This work calls for an increased coherence between climate and air quality plans at the local scale and provides a first step and a tool to support signatories, even the smallest ones, to move in this direction.

A Short-Term Air Quality Control for PM10 Levels

In this work, the implementation and test of an integrated assessment model (IAM) to aid governments to define their short term plans (STP) is presented. The methodology is based on a receding horizon approach where the forecasting model gives information about a selected air quality index up to 3 days in advance once the emission of the involved pollutants (control variable) are known. The methodology is fully general with respect to the model used for the forecast and the air quality index; nevertheless, the selection of these models must take into account the peculiarities of the pollutants to be controlled. This system has been tested for particulate matter (PM10) control over a domain located in Northern Italy including the highly polluted area of Brescia. The results show that the control system can be a valuable asset to aid local authorities in the selection of suitable air quality plans.

From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information

Abstract This study investigates the dispersion of atmospheric pollutants over a coastal region of north-western Italy by means of modelling techniques. A series of annual air quality model simulations corresponding to different emission reduction scenarios has been performed with a three-dimensional chemical transport modelling chain running at 3 km resolution. The emission reduction scenarios were used to develop bottom-up (locally produced) source-receptor relationships to perform a local source allocation analysis of the main atmospheric pollutants in a few polluted cities within the domain of interest. Results were compared with default top-down (EU-wide) source-receptor relationships, at roughly 7 km resolution. The results show the benefit of using the two sources of information in an integrated way. The analysis of the impacts of local emission reductions on concentrations and of the source allocation results reveals that nitrogen oxides concentrations are mostly affected by local emission sources, especially road transport and harbour related activities while the contribution of non-local sources is important for particulate matter (especially from industry and agriculture sources). For PM, larger scale modelling approaches (top-down) are necessary. Ideally, both a bottom-up approach for the characterisation of the local emission sources and a top-down larger scale approach to capture the impact of non-local sources would be necessary to perform an accurate source allocation, and provide support to the design of local air quality plans.

A probabilistic approach to screen and improve emission inventories

Abstract Emission inventories are generally identified as the key input to the air quality modelling chain, especially when they are used to support regulatory decisions, such as for air quality planning or for the assessment of concentration levels over a given territory. At the same time, studies point out to emission inventories as the most uncertain factor among the different components of air quality models. In a recent work, Thunis et al. (2016), developed a methodology, supported by a specific screening diagram, to identify discrepancies between emission estimates and target the pollutants and sectors for which improvements should be prioritized. Based only on the total emissions for various pollutants as input, the methodology is able to provide insight on whether these differences arise from issues related to emission factors or activities. In this work we further develop this methodology and show that the use of a probabilistic approach improves its usefulness and relevance. We motivate the use of a probabilistic approach by discussing a series of simple situations to which we apply an “intuitive reasoning”. These situations are then used as background to detail the probabilistic methodology and its main assumptions. Tested on a random set of known emission inventories, we show that the methodology performs well in reproducing the expected activities and the associated emission factors. We show that the method becomes more precise when the number of pollutants increases. Given the large differences observed between emission inventories, reducing the discrepancies between them does not only lead to more coherence but it also improves their accuracy as errors can be detected and solved. The approach is mostly designed as a screening to spot the main inconsistencies in the field of atmospheric emissions but the methodology is general and could be applied to other fields, provided that the relationships between variables fulfil similar rules as those described here.

Meteorological influences on PM2.5 and O3 trends and associated health burden since China's clean air actions

Stringent clean air actions have been implemented to improve air quality in China since 2013. In addition to anthropogenic emission abatements, the changes in air quality may be modulated also by meteorology. In this study, we developed multiple linear regression models to quantify meteorological influences on the trends in fine particulate matter (PM2.5) and ozone (O3) concentrations and associated health burden over three polluted regions of China, i.e., North China Plain, Yangtze River Delta, and Fen-wei Plain during 2014–2018, with a novel focus on the contributions of the most influential meteorological factors to PM2.5 and O3 trends as well as the meteorological contributions to PM2.5- and O3-related mortality trends. The meteorology-driven PM2.5 (O3) trends for the three regions were −0.5~−2.0 (+0.7~+0.8) μg m−3 yr−1, contributing 10– 26% (12– 18%) of the observed five-year decreasing PM2.5 (increasing O3) trends. The decreased relative humidity (increased daytime planetary boundary layer height) was identified to be the most influential meteorological factor and explained 55% (42%) of the largest meteorology-driven PM2.5 (O3) trend among all regions and seasons. The meteorology-driven decreases in PM2.5 (increases in O3) concentrations led to overall decreases in PM2.5-related (increases in O3-related) mortalities with trends of −2.2~−7.4 (+0.5~+0.9) thousand yr−1 for the three regions, accounting for 10– 26% (15– 31%) of the total decreasing (increasing) trends in PM2.5-related (O3-related) mortalities. The results emphasize the important role of meteorology in PM2.5 and O3 air quality and associated health burden over China, and have important implications for China's air quality planning. In particular, more efforts in emission control should be taken to offset the adverse effects on ozone caused by meteorology.

Source Apportionment and Integrated Assessment Modelling for Air Quality Planning

In Northern Italy a large fraction of the population is exposed to PM10 and PM2.5 concentrations that exceed the European limit values and the stricter WHO air quality guidelines. For this reason, in 2017 four Regions (Piemonte, Lombardia, Veneto, and Emilia Romagna) and the national Ministry of the Environment adopted a set of joint measures, namely the “Po Basin air quality plan”. The plan mainly tackles emission from road transport, residential heating, and agriculture. Air quality plans at regional and local scale are usually implemented defining a set of emission abatement measures, starting from experts’ knowledge. The aim of this work is to define a methodology that helps decision makers in air quality planning, combining two different approaches: Source-Apportionment techniques (SA) and Integrated Assessment Modelling (IAM). These techniques have been applied over a domain in Northern Italy to analyze the contribution of emission sources on PM10 concentration and to compute an optimal policy, obtained through a multi-objective optimization approach that minimizes both the PM10 yearly average concentration and the policy implementation costs. The results are compared to the Po Basin air quality plan impacts. The source-apportionment technique and the IAM optimization approach show intervention priorities in three main sectors: residential heating, agriculture, and road transport. The Po Basin air quality plan is effective in reducing PM10 concentrations, but not efficient, as a matter of fact the cost-effective policy at the same cost has a higher impact on air quality and on greenhouse gases emissions reduction.

Evaluation of NU-WRF model performance on air quality simulation under various model resolutions – an investigation within the framework of MICS-Asia Phase III

Abstract. Horizontal grid resolution has a profound effect on model performances on meteorology and air quality simulations. In contribution to MICS-Asia Phase III, one of whose goals was to identify and reduce model uncertainty in air quality prediction, this study examined the impact of grid resolution on meteorology and air quality simulation over East Asia, focusing on the North China Plain (NCP) region. The NASA Unified Weather Research and Forecasting (NU-WRF) model has been applied with the horizontal resolutions at 45, 15, and 5 km. The results revealed that, in comparison with ground observations, no single resolution can yield the best model performance for all variables across all stations. From a regional average perspective (i.e., across all monitoring sites), air temperature modeling was not sensitive to the grid resolution but wind and precipitation simulation showed the opposite. NU-WRF with the 5 km grid simulated the wind speed best, while the 45 km grid yielded the most realistic precipitation as compared to the site observations. For air quality simulations, finer resolution generally led to better comparisons with observations for O3, CO, NOx, and PM2.5. However, the improvement of model performance on air quality was not linear with the resolution increase. The accuracy of modeled surface O3 of the 15 km grid was greatly improved over the one from the 45 km grid. A further increase in grid resolution to 5 km, however, showed diminished impact on model performance improvement on O3 prediction. In addition, a 5 km resolution grid showed large advantage in better capturing the frequency of high-pollution occurrences. This was important for the assessment of noncompliance with ambient air quality standards, which was key to air quality planning and management. Balancing the modeling accuracy and resource limitation, a 15 km grid resolution was suggested for future MICS-Asia air quality modeling activity if the research region remained unchanged. This investigation also found a large overestimate of ground-level O3 and an underestimate of surface NOx and CO, likely due to missing emissions of NOx and CO.

Sub-kilometer dispersion simulation of a CO tracer for an inter-Andean urban valley

Air quality planning and management can benefit from the ability of numerical models to produce skillful forecasts. However, obtaining these forecasts is a continuing challenge, particularly in complex terrain. Here we examine the dispersion of traffic emissions in the Aburrá valley (located in the Colombian Andes) during an episode of severe air pollution, using the WRF-Chem and a Lagrangian model at sub-kilometer resolution. We focus on the identification of areas with higher CO concentrations (the pollutant analyzed), and examine the role of local and regional airflows in the distribution of CO inside the valley. The meteorological model performance, considering different planetary boundary layer parameterizations and grid sizes, is evaluated using available observations. Overall, the meteorological model performance is within recommended benchmarks for complex terrain. Model performance improves with increasing grid resolution for surface temperature and wind direction, but not for wind speed. Both dispersion models reproduce important features of the spatial and diurnal variability of CO in the valley, but underestimate CO concentrations throughout the simulation period. The south and southeast areas of the valley present the largest CO concentrations, associated with a prevalent northerly transport along the valley axis and reduced transport on the eastern slope. The representation of CO improves when the model adequately reproduces observed rainfall, due to effects of rainfall on boundary layer height and stability, which condition dispersion. Better simulation of transport processes is crucial for informing decisions on air quality management in critical areas such as urban valleys.

Какви административни актове са програмите за качеството на атмосферния въздух?

The article discusses recent court practice denying access to justice on clean air matters. Based on analysis of national and EU law, the article argues that air quality plans shall be considered measures of general nature and the population exposed to excessive air pollution shall be granted legal standing to challenge them before courts.