High Pollution Episodes Research Articles

A high-resolution emission inventory of air pollutants from primary crop residue burning over Northern India based on VIIRS thermal anomalies

Emissions from the crop residue burning adversely affect the regional and global air quality including public health. In this study, a district-wise comprehensive emission inventory of key pollutants (PM2.5, PM10, CO, CO2, SO2, NOx, N2O, NH3, CH4, NMVOC, EC, OC, PAH) emitted during primary crop residue burning was developed using activity data for the major agrarian states of north India for the agricultural year 2017–18. The emissions were scaled to the spatial resolution of 1 km grid to study the spatial distribution of crop residue burning activities using VIIRS Thermal anomalies datasets. An estimated 20.3 Mt and 9.6 Mt of crop residue were burned in Punjab and Haryana, resulting in an emission of 137.2 Gg and 56.9 Gg of PM2.5 and 163.7 Gg and 72.1 of PM10 Gg for respective states. The emissions of EC, OC, and PAHs were 8.6 Gg, 45.7 Gg, and 0.08 Gg in Punjab, whereas in Haryana emissions were 3.7 Gg, 17.7 Gg, and 0.03 Gg, respectively. The results show that rice and wheat crops were major contributor to residue burnt at the field (>90%) leading to the high load of atmospheric emissions in the IGP region. Further, CO2 equivalent greenhouse gas emissions were 34.8 Tg and 17.3 Tg for Punjab and Haryana, respectively. Around 30000 and 8500 active fires were detected by VIIRS over the agricultural area of Punjab and Haryana during the studied year. The GIS-based bottom-up approach using gridded emission inventory shows pollutant distribution dominates over the south-western part of Punjab and north-western region of Haryana. The proximity of these regions to Delhi and transboundary movement of emissions towards Indo-Gangetic plains causes high air pollution episodes. The high-resolution inventory of various pollutants will be useful for regional air quality models to better predict and manage the hotspot of air pollution.

Statistical Forecast of Pollution Episodes in Macao during National Holiday and COVID-19.

Statistical methods such as multiple linear regression (MLR) and classification and regression tree (CART) analysis were used to build prediction models for the levels of pollutant concentrations in Macao using meteorological and air quality historical data to three periods: (i) from 2013 to 2016, (ii) from 2015 to 2018, and (iii) from 2013 to 2018. The variables retained by the models were identical for nitrogen dioxide (NO2), particulate matter (PM10), PM2.5, but not for ozone (O3) Air pollution data from 2019 was used for validation purposes. The model for the 2013 to 2018 period was the one that performed best in prediction of the next-day concentrations levels in 2019, with high coefficient of determination (R2), between predicted and observed daily average concentrations (between 0.78 and 0.89 for all pollutants), and low root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). To understand if the prediction model was robust to extreme variations in pollutants concentration, a test was performed under the circumstances of a high pollution episode for PM2.5 and O3 during 2019, and the low pollution episode during the period of implementation of the preventive measures for COVID-19 pandemic. Regarding the high pollution episode, the period of the Chinese National Holiday of 2019 was selected, in which high concentration levels were identified for PM2.5 and O3, with peaks of daily concentration exceeding 55 μg/m3 and 400 μg/m3, respectively. The 2013 to 2018 model successfully predicted this high pollution episode with high coefficients of determination (of 0.92 for PM2.5 and 0.82 for O3). The low pollution episode for PM2.5 and O3 was identified during the 2020 COVID-19 pandemic period, with a low record of daily concentration for PM2.5 levels at 2 μg/m3 and O3 levels at 50 μg/m3, respectively. The 2013 to 2018 model successfully predicted the low pollution episode for PM2.5 and O3 with a high coefficient of determination (0.86 and 0.84, respectively). Overall, the results demonstrate that the statistical forecast model is robust and able to correctly reproduce extreme air pollution events of both high and low concentration levels.

Regional-scale modelling for the assessment of atmospheric particulate matter concentrations at rural background locations in Europe

Abstract. The application of regional-scale air quality models is an important tool in air quality assessment and management. For this reason, the understanding of model abilities and performances is mandatory. The main objective of this research was to investigate the spatial and temporal variability of background particulate matter (PM) concentrations, to evaluate the regional air quality modelling performance in simulating PM concentrations during statically stable conditions and to investigate processes that contribute to regionally increased PM concentrations with a focus on eastern and central Europe. The temporal and spatial variability of observed PM was analysed at 310 rural background stations in Europe during 2011. Two different regional air quality modelling systems (offline coupled European Monitoring and Evaluation Programme, EMEP, and online coupled Weather Research and Forecasting with Chemistry) were applied to simulate the transport of pollutants and to further investigate the processes that contributed to increased concentrations during high-pollution episodes. Background PM measurements from rural background stations, wind speed, surface pressure and ambient temperature data from 920 meteorological stations across Europe, classified according to the elevation, were used for the evaluation of individual model performance. Among the sea-level stations (up to 200 m), the best modelling performance, in terms of meteorology and chemistry, was found for both models. The underestimated modelled PM concentrations in some cases indicated the importance of the accurate assessment of regional air pollution transport under statically stable atmospheric conditions and the necessity of further model improvements.

Distribution of Trace Metals in Street Dusts and Tree Leaves and Their Source Identification in a Mid-Populated Anatolian City.

Sivas is a central Anatolian city in Turkey with mid-population. Due to its geographical structure high air pollution episodes can be observed in the atmosphere of Sivas. A study with city wide sampling campaign can help to identify the possible sources. Therefore, we aimed to investigate the multi-elemental and multi-point analysis of trace metals in street dusts and tree leaves in Sivas. In the street dusts, Ca concentration was > 10%. Fe, Cl, and K contribution was 2.5%, 1.75%, and 1.1%, respectively. The average S and Ti concentrations were between 1 and 10mg/g, I, Mn, Sr, Cr, V, Ba, Zn, Ni, Zr, and Cu were between 1 and 0.1mg/g, and W, Pb, Sn, Th, Rb, Sb, Co, Bi, As, U were between 0.1 and 0.01mg/g, in the ascending order. The same elements were investigated for vegetation. Ca, K, S, and Cl were the elements with highest contribution.

Air quality forecasting using artificial neural networks with real time dynamic error correction in highly polluted regions

Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM10, PM2.5, NO2, and O3 for the current day and subsequent 4 days in a highly polluted region (32 different locations in Delhi). The model has been trained using meteorological parameters and hourly pollution concentration data for the year 2018 and then used for generating air quality forecasts in real-time. It has also been equipped with Real Time Correction (RTC), to improve the quality of the forecasts by dynamically adjusting the forecasts based on the model performance during the past few days. The model without RTC performs decently, but with RTC the errors are further reduced in forecasted values. The utility of the model has been demonstrated in real-time and model validations were performed for the whole year of 2018 and also independently for 2019. The model shows very good performance for all the pollutants on several evaluation metrics. Coefficient of correlations for various pollutants varies from 0.79–0.88 to 0.49–0.68 between the Day0 to Day4 forecasts. Lowest deterioration of performance was observed for ozone over the four days of forecasts. Use of RTC further improves the model performance for all pollutants.

Assessment of source contributions to organic carbon in ambient fine particle using receptor model with inorganic and organic source tracers at an urban site of Beijing

The collections of ambient fine particles were carried out in the period of January 16 to 31, 2013, in Beijing. The levels of carbonaceous aerosols (i.e., organic carbon and elemental carbon) in fine particles were determined. The chemical compositions of primary source tracers including alkanes, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(e)pyrene, benzo(ghi)perylene, picene, 17a(H)-22,29,30-trisnorhopane, levoglucosan, Al and Fe in fine particles were analyzed. Chemical mass balance (CMB) model coupled with inorganic and organic source tracers was utilized to estimate daily contributions of primary sources (e.g., coal combustion, biomass burning, dust source, mobile source) to organic carbon (OC). The sensitivity analysis of specific primary source was carried out in order to obtain the accurate contribution from primary sources. Our study indicated that CMB with inorganic and organic source tracer method was efficient for apportioning primary sources of OC in Beijing during high air pollution episodes.

Black carbon and biomass burning associated high pollution episodes observed at Doon valley in the foothills of the Himalayas

The Doon valley located in the foothills of the Indian Himalayan region, is recently undergoing rapid urbanisation. Hence, in this study, we investigate the variation of black carbon mass concentration (BC) and biomass burning generated black carbon mass concentration (BCbb) for one year (October 2017 to September 2018) using ground-based observations. We also present here the relationship of BC with meteorological parameters and effect of transport. Using three different case studies we explain the dominant role of local meteorological conditions and effect of long-range transported aerosols impacting the study site in different seasons. Satellite and reanalysis datasets were also used to strengthen the analysis. A seven channel Aethalometer (AE-33) was used to measure real-time BC at the study site. Seasonal analysis indicates a winter maximum (9.45 ± 2.65 μg m−3) followed by post-monsoon (6.94 ± 1.52 μg m−3), pre-monsoon (5.35 ± 1.46 μg m−3), and lowest in the monsoon season (3.36 ± 0.62 μg m−3). The daily mean ground-based BC had a moderately positive correlation with Modern-Era Retrospective analysis for Research and Applications, version-2 (MERRA2) BC (r = 0.52) and Copernicus Atmosphere Monitoring Service (CAMS) BC (r = 0.74), these correlations get better when compared with monthly datasets. Effect of local emissions and long-range transport was studied intricately using wind rose, Conditional Bivariate Probability Function (CBPF), and Concentration Weighted Trajectory (CWT) analysis. Case studies of high BCbb specifically in the months of November, January, and May precisely segregated the dominant effect of meteorology and transport phenomena in different seasons. Primarily during the months of November and May, the long-range transport of aerosols from regions dominated by crop residue burning and forest fires, respectively, increased the BCbb concentration over the study site. While during January, emissions generated from local burning activities for space heating and cooking, aided by lower temperatures, increased the BCbb, indicating the dominant role of meteorology. These results were further substantiated through the aerosol subtypes acquired from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite.

Impact of Synoptic Weather Types on Ground-Level Ozone Concentrations in Guangzhou, China

Although precursor concentrations were reduced by emission control in Guangzhou, southern China from 2006 to 2016, ground-level O3 concentrations increased, forming potential risks to human health. This study explored the impacts of large-scale synoptic weather circulations on O3 concentration in Guangzhou, in a particular focus on high O3 pollution episodes. Twelve local weather types were clustered based on Lamb-Jenkinson weather types (LWTs). Analyses showed that LWTs strongly impacted daily O3 concentrations: A, AS, CN, and N+ weather types were likely associated with high ozone concentrations, while the ozone levels were relatively low under C, CE, CS, and S+ types. LWTs could explain 30–40% of the inter-annual variability of O3 concentration during the dry season. Numerical model simulations further demonstrated that continuous type A weather was the leading LWT correlated with high O3 concentrations, while type C weather was the predominant type correlated with low O3 concentrations. CMIP5 model results showed that occurrences of weather type A would increase by about 25% in the high emission scenario over the 2020–2069 period, which might worsen the O3 pollution in Guangzhou in the future. The increase in frequency weather type A would not be significant under the low emission scenario during the same period. Therefore, we should strictly implement the global emission reduction plan to prevent the change of weather circulation caused by climate change from aggravating ozone pollution in the future. The strong link between O3 concentrations and LWT frequencies makes the daily occurrence of LWTs a useful predictor for episodes of high O3 pollution and makes annual frequencies of LWTs good indicators of the inter-annual variability of the O3 concentration. These results are useful in efforts to predict O3 concentrations, providing a reliable weather forecast is available.

PM2.5 forecasting in Coyhaique, the most polluted city in the Americas

Coyhaique is a southern Chilean city with a population of approximately 64,000 habitants. In spite of its small size, Coyhaique has been identified as the city with highest annual PM2.5 concentrations of the Americas (including south America, central America and north America). Episodes of high pollution are concentrated on the fall- winter season when meteorological conditions do not favor atmospheric particle dispersion and extended use of wood stoves is responsible for more than 99% of the emissions. In Chile, the 24 h average of PM2.5 concentration is classified in four ranges: fair, bad, very bad and critical. We have developed a neural network model and a linear model aimed to forecast the maximum of the 24 h moving average one day in advance. Input variables for the models are hourly values of PM2.5 at 18 h and 19 h of the present day, measured and forecasted temperature, wind speed and precipitation and measured values of NO2, CO and O3 concentrations. The neural network model is slightly more accurate than the linear model. We are able to anticipate the observed range in 75% of the cases, and critical days in 84% of the cases.

Self-Organizing Maps to Validate Anti-Pollution Policies

Abstract This study presents the application of self-organizing maps to air-quality data in order to analyze episodes of high pollution in Madrid (Spain’s capital city). The goal of this work is to explore the dataset and then compare several scenarios with similar atmospheric conditions (periods of high Nitrogen dioxide concentration): some of them when no actions were taken and some when traffic restrictions were imposed. The levels of main pollutants, recorded at these stations for eleven days at four different times from 2015 to 2018, are analyzed in order to determine the effectiveness of the anti-pollution measures. The visualization of trajectories on the self-organizing map let us clearly see the evolution of pollution levels and consequently evaluate the effectiveness of the taken measures, after and during the protocol activation time.

Chemical Composition and Size Distribution of Aerosol Particle in High Polluted Periods

In this study, PM10 and PM2.5 mass concentrations were investigated in Busan Metropolitan City on high pollution episode periods. PM10 and PM2.5 were collected from December 2013 to January 2015. The aerosol samples were collected during Asian dust season and high polluted periods in winter respectively. The collected samples were analyzed by some techniques such as comparison of mass concentration, correlation analysis, chemical composition and particle size distribution. Mass concentration of PM10 in spring season ranged from 62.6 μg/m3 to 131.5 μg/m3. High PM10 and PM2.5 concentration on spring were contributed mainly by Asian-Dust. In wintertime, PM10 and PM2.5 high concentrations were almost contributed by anthropogenic source such as house heating and stagnant meteorological conditions. But, PM2.5 mass concentration did not show any difference according to sampling sites. SEM-EDS results showed that high polluted periods have different chemical composition elements such as Al, Ca, Mo, Zn in spring and Cu, Cr, Mn, Ti, Zr in winter. Particle number distribution on Asian dust season showed that particle counts were increased to the factor of 2 in the size of 5 μm , the size of 3 μm was increased to the factor of 2, the size of 1μm was increased the factor of 1.6 respectively. On the other hands, particle counts in the size of 1 μm and 0.5 μm were decreased to the factor of 1.6 and 0.3 μm was considerably decreased to the factor of 3.5. Finally, it is possible to establish the proper PM pollution control strategy to evaluate levels of high PM10 and PM2.5 pollution periods, chemical composition and size distribution by detail analysis.

Impact of restrictions to tackle high pollution episodes in Madrid: Modal share change in commuting corridors

With the increasing concern about environmental problems in metropolitan areas, policy-makers are establishing restrictions on private vehicles in city centers to mitigate air pollution levels. Like many other European cities, Madrid fails to fulfill repeatedly the legal limits on pollutant concentrations established by the European Commission. Accordingly, in order to comply with acceptable air quality levels, Madrid City Council passed a protocol to address high NO2 pollution episodes. This paper aims at gaining deeper insight into how emission abatement plans foster a more sustainable mobility in suburban trips. To that end, a discrete choice analysis is conducted to explore the influence of the different restriction stages on modal share in a commuting corridor. Based on traffic counts and transit entrances, two multinomial grouped logit models are calibrated. Moreover, differences across trip frequencies are studied in order to determine to what extent a modal shift from private vehicles to more environmentally-friendly modes of transport occurs. The paper concludes that modal choice in suburban trips is influenced by the mobility restrictions adopted. Nevertheless, this effect shows to be fairly limited in the case of the Madrid NO2 Protocol. Therefore, more severe measures should be implemented to achieve a greater modal shift towards more sustainable transport modes.

Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes.

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM2.5 (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m−3, are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.

2018년 11월 광주 지역에서 발생한 미세먼지 고농도 사례 시 수용성 성분의 크기 분포 조사

In this study, mass size distributions of ambient aerosol particles and their water-soluble organic and inorganic species were investigated under high pollution episode. To achieve the aim of this study, 24-hr integrated size-segregated aerosol samples were collected at an ubran site of Gwangju between November 02 and 16, 2018, and analyzed for mass, water-soluble organic carbon (WSOC), and ionic species. During study period, a high pollution episode of fine particulate matter (PM) occurred between November 02 and 07, possibly due to high pressure developed around Korean peninsula, extremely stable air conditions, and long-range transportation of aerosol particles from North Korea and northeastern China region. On November 02~03 when influence of local pollution dominated, ambient aerosol particles, WSOC, NO3-, SO₄2-, and NH4+ exhibited bi-modal size distributions with a condensation mode at a particle size of 0.32 μm and a droplet mode at a particle size of 1.0 μm in PM1.8. While, between November 05 and 07 when long-range transportation of PM was mixed with locally-produced pollution, they showed very pronounced droplet mode size distributions at 1.0 μm, without a condensation mode. NO3- concentration in PM1.8 during pollution episode increased significantly from 5.9 μg/m³ on November 02 to 21.5μg/m³ on November 06, while SO₄2- in PM1.8 increased from 1.6 to 5.8 μg/m³, indicating the dominance of local pollution during pollution episode. Furthermore, for WSOC, NO3-, SO₄2-, and NH4+, fraction of droplet mode size (PM0.55-1.8) to PM1.8 increased gradually as PM pollution got increased, while the condensation mode (PM0.17-0.32) contribution decreased. In addition, it was demonstrated that relative humidity could be an important factor promoting formation of droplet mode WSOC, NO3-, and SO₄2-.

Contributions of local and regional sources to PM2.5 and its health effects in north India

Air quality in India, especially in north India, is increasingly severe due to rapid growth of industrialization and urbanization in past decades. Very few studies were conducted in the recent past to quantify regional contributions to high pollutant concentrations for designing effective control strategies. In this paper, source-oriented versions of Community Multi-scale Air Quality (CMAQ) model were applied to quantify the contributions of nine source regions (Delhi, Haryana & Rajasthan, Uttar Pradesh & Uttarakhand, Himachal Pradesh & Punjab, Central India, West India, South India, East & Northeast India and Outside India) to fine particulate matter (PM2.5) and its components including primary PM (PPM) and secondary inorganic aerosol (SIA) i.e. sulfate, nitrate and ammonium ions, in Delhi and three surrounding cities, Chandigarh, Lucknow and Jaipur in 2015. PPM is dominated by local sources (>70%), with Delhi as the top contributor in Delhi contributing to a maximum of 90 μg/m3 in winter. Unlike PPM, SIA contributions from non-local sources are more significant. Delhi, Haryana & Rajasthan, Uttar Pradesh & Uttarakhand, and Himachal Pradesh & Punjab contribute to ~4 μg/m3 SIA concentrations in North India. In Delhi, local sources contribute to over 70% of total PM2.5, but the non-local sources are still important (over 30%) especially in winter. Furthermore, local emissions contribute less in highly polluted days compared to less polluted days indicating the importance of regional transport on high pollution episodes in Delhi. Overall, while local sources contribute to 1.495 × 104 premature deaths in Delhi, non-local sources contribute to 0.483 × 104 premature deaths.

Repeated extreme particulate matter episodes due to fireworks in Iceland and stakeholders’ response

Abstract Extreme pollution associated with residential firework celebrations is recognized as an important health and environmental issue. Despite this knowledge, consumer fireworks continue to be allowed in many countries around the world. This paper explores the physical and societal factors contributing to extreme recreational firework pollution, as well as stakeholders' perspectives on the resultant pollution and possible mitigation. Metropolitan Reykjavik (217 000 inhabitants) is taken as an example of an affluent, well informed community with a relatively recent history of fireworks displays. Largely non-regulated public access to fireworks has resulted in exceedance of the European air quality standard of PM10 (50 μg/m3), and hazardous hourly concentration (>500 μg/m3) every other year. In the second hour of New Year's Day (NYD) 2018, hourly particulate matter records, 4042 μg/m3 PM10 and 3014 μg/m3 PM2.5, were set in a residential, suburban area. Concentrations of potential toxic metals increased by a factor of 104 (Cu), 96 (Sr), 27 (Ba) during the celebrations compared to the preceding days. The reasons why these high pollution episodes continue to occur, based on stakeholders' interviews, are traced back to a series of societal and regulatory hurdles: (1) Conflicting interests at governmental, municipal and entrepreneurial levels because fireworks are an entertaining spectacle for locals and tourists and a major fundraiser for not-for-profit organizations (NPOs), and yet pose an unacceptable public burden in terms of air pollution. (2) The lawful public access to fireworks severely limits stakeholder involvement and air quality mitigation actions. (3) Implementation of fireworks policy is dispersed among multiple stakeholders. These factors promote stakeholder passiveness towards pollution mitigation: Continuing public awareness campaigns with the goal of slowly weaning the public off fireworks were favored over restricting public use to fireworks. Future opportunities lie in holistic policy making, using the United Nations Sustainable Development Goals as a roadmap; and in clarifying the risk of extreme particulate matter episodes in the relevant regulations, e.g. by accounting for the level of exceedances (concentration value divided by ambient air quality standard), and the nature of the particles (size, morphology, chemical composition).

Analysis of a severe air pollution episode in India during Diwali festival – a nationwide approach

Air pollution events have been a subject of major concern worldwide in recent years, since their frequency and intensity is increasing with time. An alarmingly high air pollution episode was observed in some parts of India during late October to the end of November in the year 2016. The Air Quality Index was observed to vary from poor to severe in northern and western parts of India, while it was satisfactory to moderate in the east and south of the country. The causes of this observed high pollution episode are examined in this paper. Apart from the Diwali effect on air pollution the study findings indicated the importance of biomass burning over India.

CHARACTERIZATION OF VOLATILE ORGANIC COMPOUNDS AND ODOUR POLLUTION IN ATMOSPHERE AT OIL AND GASOLINE HANDLING AND PROCESSING IMPACT ZONE

Air quality pollution problem is still one of the crucial points for citizens in Europe for already receiving increasing attention, particularly because of the major European cities 10 and more years. Although the EU's long-term goal is to achieve levels of air quality that do not impact and risks to human health and the environment, many of member states still didn`t reach stated goals. Additionally, to gaseous pollutants, recently specific type of pollution, - odour, seems to become more important. Usually in order to determine pollution levels, national, municipal and private monitoring equipment is used. For this research municipal monitoring site in Riga (Latvia), at Milgravja Street 10, controlling gaseous pollutants (SO2, O3, BTX, PM10) and airborne particulate matter, and private monitoring results from Riga, Milgravja Street 16, where odour pollution was obtained, are analysed. Distance between both stations are just 500 m. Measurements at municipal monitoring site is obtained by DOAS and gravimetric sampling, while at Milgravja 16 by photoionization method or so-called “electronic nose”. Monitoring results in municipal station show that in 2017 the average benzene concentration was 4,87 ug/m3, toluene – 8,89 ug /m3 and xylene – 5,07 ug/m3, while the odour pollution level does not exceed 5 odour units. In general estimation of pollution averaged annually do not show and explain variability of pollution levels. It`s well known that high BTX and odour pollution episodes occur in shorter periods, thus short term limit values would be useful in order to characterize short term effects on human health and well-being.

The transport of PM10 over Cape Town during high pollution episodes

This study investigates the influence of PM10 from remote sources on PM10 episodes over the city of Cape Town. For the study, we analysed observation data from Cape Town's air quality monitoring stations as well as high-resolution simulation data from the Weather Research and Forecasting model with Chemistry (WRF-Chem). The observation data were used to identify PM10 episodes during the study period (2008–2014) and WRF-Chem was applied to simulate the atmospheric conditions and PM10 transport over southern Africa during each episode. The capability of WRF-Chem to simulate the wind and PM10 concentration over Cape Town was quantified and the paths of air parcels over Cape Town during each episode were tracked. Results of the study show that WRF-Chem gives a realistic simulation of observed wind (speed and direction) over the city during the episodes, but the model struggles to reproduce the observed PM10 concentration. For most episodes, the magnitude of the simulated PM10 is lower than the observed due to lack of local emissions in the simulations. In some cases, the model reproduces the peak in PM10 concentration some days earlier or later than observed. The simulations show that most air parcels over Cape Town during the episodes have travelled over major dust source regions (i.e., the Kalahari or Namib Desert) before reaching the city. Most of episodes are associated with a southward transport of a plume of PM10 from the north-west coast of southern Africa to Cape Town. This PM10 plume is induced by a coastal trough and a continental high pressure system. In some cases, local topography influences the intrusion of the PM10 plume into Cape Town by blocking some of the pollution, thereby minimising the amount of PM10 that reaches the city. Results of the study suggest that the transport of PM10 from the north-west coast of southern Africa may contribute to PM10 episodes in Cape Town.

What caused the extreme CO concentrations during the 2017 high-pollution episode in India?

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), launched 13 October 2017, has been measuring carbon monoxide (CO) concentrations in the Earth's atmosphere since early November 2017. In the first measurements, TROPOMI was able to measure CO concentrations of the high-pollution event in India of November 2017. In this paper, we studied the extent of the pollution in India, comparing the TROPOMI CO with modeled data from the Weather Research and Forecasting model (WRF) to identify the most important sources contributing to the high pollution, both at ground level and in the total column. We investigated the period 11–19 November 2017. We found that residential and commercial combustion was a much more important source of CO pollution than the post-monsoon crop burning during this period, which is in contrast to what media suggested and some studies on aerosol emissions found. Also, the high pollution was not limited to Delhi and its direct neighborhood but the accumulation of pollution extended over the whole Indo-Gangetic Plain (IGP) due to the unfavorable weather conditions in combination with extensive emissions. From the TROPOMI data and WRF simulations, we observed a buildup of CO during 11–14 November and a decline in CO after 15 November. The meteorological conditions, characterized by low wind speeds and shallow atmospheric boundary layers, were most likely the primary explanation for the temporal accumulation and subsequent dispersion of regionally emitted CO in the atmosphere. This emphasizes the important role of atmospheric dynamics in determining the air quality conditions at ground level and in the total column. Due to its rapidly growing population and economy, India is expected to encounter similar pollution events more often in future post-monsoon and winter seasons unless significant policy measures are taken to reduce residential and commercial emissions.