Indicative measurements of air pollution in Ukraine within the limits of legal harmonization with european standards (using the example of Kharkiv and Kropyvnytsky cities)

This research was undertaken with support from the Australian National University College of Health and Medicine, and the assistance of resources from the Centre for Air pollution, energy and health Research (CAR). We used the CAR Data and Analysis Technology platform (https://cardat.github.io) to analyse data

COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown

Background.In recent years, poor urban air quality in Delhi, India has gained significant attention. Episodic events including crop stubble burning and Diwali celebrations are considered major factors in the worsening quality of ambient air.Objective.This study aimed to investigate spatial and monthly variation as well as the role of episodic events in ambient air quality in Delhi, including the ‘Great Smog' month of November 2017.Methods.Monitoring of air pollutants (particulate matter (PM10, PM2.5, PM1) and nitrogen dioxide (NO2)) was carried out at three distinct locations of Delhi from April 2017–February 2018. The concentration of NO2 was measured using a modified Jacob and Hochheiser method and PM was measured using a GRIMM aerosol spectrometer. Air quality index was also determined to identify the effects of air pollution on human health.Results.Overall, the levels of air pollution were found to be approximately 2.1–3.2 times higher along a traffic intersection and about 1.4–2.0 times higher in a commercial area compared with an institutional area. The highest average monthly concentrations of PM10, PM2.5, PM1 and NO2 were 768, 374, 298 and 149 μg/m3, respectively, during the Great Smog month of November 2017. November and August were recorded as the most polluted and cleanest months, respectively, in the city. Generally, poor to severe categories of the air quality index (AQI) were obtained from October to February. Higher concentrations during November were attributed to stubble burning in the nearby states of Delhi with the additive effect of fireworks during Diwali celebrations.Conclusions.Severe ambient air quality as observed in the present study is a serious matter of concern for the health of Delhi's population. To control spikes in poor air quality during episodic events, it is imperative to raise awareness among farmers regarding the severe health hazards of stubble burning.Competing Interests.The authors declare no competing financial interests.

The purpose of this analysis is threefold. We first examine the extent to which a longer series of data improves our understanding of air pollution on human mortality in the Atlanta, GA, area by updating the findings presented in Klemm and Mason (J. Air Waste Manage. Assoc. 2000, 50, 1433-1439) and Klemm et al. (Inhal. Toxicol. 2004, 16 (Suppl 1), 131-141) with 7.5 additional years of data. We explore estimated effects on two age groups (<65 and 65+) and four categories of cause of death. Second, we investigate how enlarging the geographic area of inquiry influences the estimated effects. Third, because some air quality (AQ) measures are monitored less frequently than daily, we investigate the extent to which AQ measurement frequency can influence estimates of relationships with human mortality. Our analytical approach employs a Poisson regression model using generalized linear modeling in S-Plus to estimate the relationship between daily AQ measures and daily mortality counts. We show that the estimated effects and their associated t values vary by year for nine AQ measures (particulate matter with aerodynamic diameter ≤2.5 μm [PM2.5], elemental carbon [EC], organic carbon [OC], NO3, SO4, O3, NO2, CO, and SO2). Several of the estimated AQ effects show downward trends during the 9-year period of study. The estimated effects tend to be strongest for the AQ measurement during the day of death and tend to decrease with additional lags. Enlarging the geographic area from two to four counties in the metropolitan area decreased the estimated effects, perhaps partly due to the fact that the measurement site is located in one of the two original counties. Estimated effects utilizing data as if the AQ were only measured every 3rd or every 6th day each week or twice per week vary from lower to higher than that estimated with daily measurements, although the t values are lower, as expected. IMPLICATIONS The ranges of published estimated effects of air quality measures on mortality suggest both underlying complexities and statistical variability. Decision-makers may be uncertain as to how to best use subgroup estimates; however, they should be aware of random fluctuations and underlying trends. Results from three scenarios of reduced frequencies of air quality measure monitoring show that estimated effects may differ substantially from those estimated from daily measurements. Although individual annual estimates will fluctuate, analysis of their trends provides information not available from analyzing the entire period as a whole. Daily measurements are essential for precise estimates of health effects.

Most cities in China are experiencing severe air pollution due to rapid economic development and accelerated urbanization. Long-term air pollution data with high temporal and spatial resolutions are needed to support research into physical and chemical processes that affect air quality, and the corresponding health risks. For the first time, data on PM10, PM2.5, SO2, NO2, O3 and CO concentrations in 23 ambient air quality automatic monitoring stations and routine meteorological were collected between January 2014 and December 2016 to determine the spatial and temporal variation in these pollutants and influencing factors in Chengdu. The annual mean concentrations of PM2.5 and PM10 exceeded the standard of Chinese Ambient Air Quality and World Health Organization guidelines standards at all of the stations. The concentrations of PM10, PM2.5, SO2 and CO decreased from 2014 to 2016, and the NO2 level was stable, whereas the O3 level increased markedly during this period. The air pollution characteristics in Chengdu showed simultaneously high PM concentrations and O3. High PM concentrations were mainly observed in the middle region of Chengdu and may have been due to the joint effects of industrial and vehicle emissions. Ozone pollution was mainly due to vehicle emissions in the downtown area, and industry had a more important effect on O3 in the northern area with fewer vehicles. The concentrations of PM10, PM2.5, NO2 and CO were highest in winter and lowest in summer; the highest SO2 concentration was also observed in winter and was lowest in autumn, whereas the O3 concentration peaked in summer. Haze pollution can easily form under the weather conditions of static wind, low temperature and relative humidity, and high surface pressure inside Chengdu. In contrast, severe ozone pollution is often associated with high temperature.

In India, Indo-Gangetic Plains (IGP) is becoming the hotspot of air pollution due to increasing anthropogenic activities such as rapid industrial growth, infrastructure development, transportation activities, and seasonal practice of crop residue burning. In the current study, seasonal variation in ambient air quality for 14 parameters, i.e., particulate matter (PM), trace gases, and volatile organic compounds (VOCs), along with meteorological parameters, was studied in 21 districts of the Haryana state for year 2019, situated in IGP. To analyze spatial variation of pollutants, ambient air quality data of 23 continuous ambient air quality monitoring stations were divided into three zones based on ecology and cropping pattern. All the zones showed annual mean PM10 and PM2.5 concentrations much higher than national ambient air quality standards. Annual mean PM10 concentration (±standard deviation) in Zones-1, 2, and 3 was 156±86, 174±93, and 143±74 μg m-3, whereas for PM2.5 was 71±44, 85±54, and 78±47 μg m-3. The results showed a considerable seasonal variation in the concentration of all pollutants. Most of the pollutants peak in the post-monsoon season, followed by winters in which crop residue burning predominates in many parts of the Haryana. PM10 concentrations increased by 65-112% and PM2.5 concentrations increased by 131-147% in the post-monsoon season compared to monsoons. The post-monsoon season showed the highest concentration of PM10, NO, and toluene (Zone-1); and PM2.5, NH3, CO, and benzene (Zone-2); whereas in winters, SO2 (Zone-1); ethylbenzene, m,p-xylene, and xylene (Zone-2); and NO2 and NOx (Zone-3) showed the maximum pollution levels. The O3 concentration was highest in the pre-monsoon season (Zone-1). The satellite-based fire counts and PCA results show a significant influence of crop residue burning in the post-monsoon season and solid biomass burning in winters on Haryana's air quality. The study could help to understand seasonal variation in ambient air quality and the influence of factors such as crop residue burning in the IGP region, which could help to formulate season-specific control measures to improve regional air quality.

Chapter 19 - Measurement of Air Quality

Introduction. The study is relevant due to the necessity to adequately assess the effectiveness of activities performed within the “Clean Air” Federal Project using ambient air quality and public health risks as key performance indicators. The purpose of the work was to comparatively assess ambient air quality and airborne health risks for Bratsk population over 2020 –2022 when activities planned within the “Clean Air” Federal Project were being implemented in the city. Materials and methods. The analysis was based on data obtained by instrumental measurements of ambient air quality performed within social-hygienic monitoring programs. More than thirty thousand measuring results were considered; they covered 20 chemicals and were taken at two monitoring posts. Carcinogenic and acute and chronic non-carcinogenic health risks were assessed in conformity with the existing regulatory documents. Results. Between 2020 and 2022, a decrease in ground levels of particulate matter, sulfur dioxide, nitrogen oxides, hydrogen chloride, and some other technogenic chemicals was registered at social-hygienic monitoring posts in Bratsk. These chemicals were subject to specific activities stipulated by the “Clean Air” Federal Project and performed by economic entities. However, public health risks remained impermissible. Thus, an acute risk of respiratory diseases equaled 3.24 during 2022 and a chronic risk of respiratory diseases reached 5.43 in the same year whereas their permissible level should not exceed 3.0. Limitations. Analysis is possible only if there is a sufficient number of instrumental studies at points located in residential buildings to assess the risk (least 300 one-time or 75 daily samples for each impurity). Conclusion. The study results indicate still necessary to take further efforts to provide a safer environment for Bratsk population and to continue monitoring of chemicals that create impermissible health risks. The most important task is to manage systemic operative data exchange between systems for environmental and social-hygienic monitoring to provide adequate assessment of a sanitary-hygienic situation as well as objective evaluation of key performance indicators of the “Clean Air” Federal Project

Design of air quality monitoring network of Luanda, Angola: Urban air pollution assessment

Abstract. To improve dust storm identification over the western United States, historical dust events measured by air quality and satellite observations are analyzed based on their characteristics in data sets of regular meteorology, satellite-based aerosol optical depth (AOD), and air quality measurements. Based on the prevailing weather conditions associated with dust emission, dust storm events are classified into the following four typical types: (1) The key feature of cold front-induced dust storms is their rapid process with strong dust emissions. (2) Events caused by meso- to small-scale weather systems have the highest levels of emissions. (3) Dust storms caused by tropical disturbances show a stronger air concentration of dust and last longer than those in (1) and (2). (4) Dust storms triggered by cyclogenesis last the longest. In this paper, sample events of each type are selected and examined to explore characteristics observed from in situ and remote-sensing measurements. These characteristics include the lasting period, surface wind speeds, areas affected, average loading on ground-based optical and/or air quality measurements, peak loading on ground-based optical and/or air quality measurements, and loading on satellite-based aerosol optical depth. Based on these analyses, we compare the characteristics of the same dust events captured in different data sets in order to define the dust identification criteria. The analyses show that the variability in mass concentrations captured by in situ measurements is consistent with the variability in AOD from stationary and satellite observations. Our analyses also find that different data sets are capable of identifying certain common characteristics, while each data set also provides specific information about a dust storm event. For example, the meteorological data are good at identifying the lasting period and area impacted by a dust event; the ground-based air quality and optical measurements can capture the peak strength well; aerosol optical depth (AOD) from satellite data sets allows us to better identify dust-storm-affected areas and the spatial extent of dust. The current study also indicates that the combination of in situ and satellite observations is a better method to fill gaps in dust storm recordings.

The HEAPS study (Health Effects of Air Pollution in Antwerp Schools) was set up to evaluate the health impact of traffic related air pollution on school children in Flanders, Belgium. The study comprises the biomonitoring of 130 children (aged 6-12) from 2 schools and simultaneous air quality monitoring. All air quality and health measurements were performed in both spring and autumn 2011. One of the schools was located next to a busy road of 20,000 vehicles/day, the other school was located at an urban background location. Air quality measurements were performed at the schools (UFP, PM, BC, NOx, and O3), at a selection of 40-50 home locations (BC, NO2) and while in transport (UFP, BC). The measurements were translated into exposure estimates in different time frames: respectively evaluating acute health effects, subacute effects (1 to 10 days before sampling), and long term health effects. The land use regression technique was applied to estimate BC and NO2 concentrations at unmeasured locations. In the exposure assessment, the time-activity pattern of the children and concentrations in different micro-environments were taken into account. Next to this, personal NO2 samplers were carried by the children during one week. Health assessment consisted of measurements of fractional exhaled nitric oxide (FeNO), exhaled breath condensate (EBC) markers (pH, 8-isoprostane, cytokines), and urinary 8-oxo-2'-deoxyguanosine (8-oxodG). Additionally, allergic inflammation in the nose was assessed in spring with a Rhinostick, while in autumn the Rhinostick was used to analyze nasal concentrations of IL-8, eosinophilic cation protein (ECP), and tryptase. ISAAC-type questionnaires on asthma, rhinitis and eczema symptoms were distributed amongst all children attending one of the schools. Acknowledgement: the study was financed by the Flemish Environment Agency (VMM), the Flemish Administration of Environment, Nature and Energy (LNE), and the Flemish Agency for Care and Health (VAZG).

Reduction in air quality in urban areas is a concern due to increased emissions from vehicles, and commercial and industrial activities. The high concentration of air pollutants can cause various adverse health effects. This study aims to characterize urban air pollution levels by measuring NO2, SO2, and PM10 in Jodhpur city, the Sun City of Rajasthan (India). Air quality index (AQI) and exceedance factors are also calculated to determine the current air quality status and relate possible health impacts associated with air pollutants’ existing levels. Raw data is collected̄ from 10 air quality monitoring stations situated in the critical city’s areas, namely DIC Office, Housing Board, Kudi Woman Police Station, Maha Mandir, RIICO Office Basni Industrial Area, Sangariya Police Sub-station, Shastri Nagar Police Station, Sojati Gate, Soorsagar Police Station, and Collectorate Office for the study period from January 1, 2019, to December 31, 2019. Seasonal and annual variations in the levels of concentration of the three criteria pollutants have been studied along with AQI. The annual average concentrations of gaseous pollutants (SO2 and NO2) were 6.76 and 24.82 μg/m3, respectively. The concentration of NO2 and SO2 was well under the NAAQS prescribed limits defined by India’s CPCB. PM10 was revealed as the primary pollutant with an annual average concentration of 219.67 μg/m3. The concentration of PM10 was violating the NAAQS in all seasons, making it responsible for the bad air quality of the Jodhpur city during the observation period. The monthly and seasonal data analysis indicates significant variations in the concentration level of all selected parameters for the study. The computed AQI reveals a gradually increasing trend with a range of 71.00–430.0, signifying the prevalence of moderate to heavy pollution levels. Comparing the exceedance factors (EFs) divulges that suspended and respirable particulate matters are the most significant polluting agents, causing maximum deterioration in the ambient air quality. Thus, it can be concluded from the study that significant air pollution in Jodhpur city was due to particulate matters.KeywordsAir qualityAir quality indexExceedance factorNAAQSAir pollution

In this report the current air quality in Finland has been assessed with air quality measurement data and satellite observations. The assessment of ambient air concentrations included following air impurities: NO2, NOx, PM10, PM2,5, SO2, CO, O3, benzo(a)pyrene, benzene, Pb, As, Cd ja Ni. For these pollutants air quality assessment thresholds are given in air quality legislation (2008/50/EY, 2004/107/EY). Assessment has been performed for air quality zones. The main data set included air quality measurements performed in Finland during 2015–2019. Satellite observations were used as an objective assessment tool in analysis of the spatial variation of NO2 and CO concentrations. Air quality measurements show that air quality has improved in Finland in many respects. Especially the need to monitor NO2 and PM10 with continuous measurements has decreased. Growing understanding of national benzo(a)pyrene concentrations has increased the monitoring needs. Efforts to decrease ozone levels still requires international actions. SO2, CO, benzene and heavy metal concentrations are on a low level in Finland outside industrial areas and other assessment methods than continuous monitoring can be used, and the number of continuous monitoring sites has already decreased. Satellite-based concentrations of nitrogen dioxide and carbon monoxide as well as their spatial variation in Finland were analyzed using observations from the TROPOsperic Monitoring Instrument (TROPOMI). The analysis of CO over Finland was carried out for the first time in this project. Results show that overall annual CO concentrations over Finland are low and spatial variability is small. Also, NO2 concentrations over Finland are rather low, but spatial patterns are more clearly visible. The highest NO2 concentrations are observed over the largest cities. By establishing a relationship between ground-based and satellite total column concentrations, surface concentrations of NO2 and CO were estimated from the satellite data for the zones. The satellite-based estimate for annual NO2 surface concentration over Helsinki metropolitan area is 28 μg/m3, and for the rest of Finland mostly between 10–15 μg/m3. For CO the differences between monitoring areas are small, with estimates varying between 160–164 μg/m3 or in other words about 0,16 mg/m3.

Various local and international research has been published on the effects of COVID-19 lockdown on ambient air quality. In most cases, a reduction in ambient NOx and PM concentrations have been observed with varying changes in ambient SO2 levels. Secunda, located in the Highveld Priority Area in Mpumalanga, South Africa is known for its large industrial facilities utilising coal as primary feedstock. The towns of Secunda and eMbalenhle provide the majority of the workforce to Sasol and has therefore been the focus of this study. The ambient air quality in the Secunda region was assessed due to the changes in human behaviour during lockdown, familiarity with the Sasol facility and the strategic locations of ambient air quality stations.Results show a clear decrease in ambient CO, NO2 and PM concentrations, especially during the first two weeks of lockdown. Only subtle changes were observed for ambient H2S and SO2 pollutant concentrations at the ambient monitoring stations. An increasing trend in all ambient species was observed towards the end and post lockdown, in contrast to declining ambient temperatures with the onset of winter. This is also contrary to the reduction in emissions from the factory that conducted annual maintenance in the month following lockdown (phase shutdown). This article concludes that human behaviour has a material local ambient impact on CO, NO2 and PM pollutant species, while H2S concentration profiles are more directly related to the industrial complex’s levels of activity. Ambient SO2 trends did not show a similar correlation with the facility’s activities (as H2S), but a stronger correlation was observed with the diverse local and regional sources in close proximity to Secunda and eMbalenhle. The influence of better dispersion especially on a local scale, brought about by more effective emission heights, is considered material. Moreover, meteorological factors, on local air quality, has been shown to be a material contributor to observed ambient air quality levels in the study domain

To investigate the exposure to major air pollutants of runners while running in Beijing, the concentrations of PM2.5, CO, O3, and NO2 were constantly monitored at typical park running areas (Tiantan Park and Olympic Sports Center), roadside running areas (Qianmen East Street and Yongding Inner Street), and a background area (Dinglin) during April, June, and October of 2016 and in January 2017. The concentration variation characteristics for the pollutants in different areas and at different times were analyzed. Using a numerical model of human respiratory exposure, 102 joggers were studied, as was the temporal-spatial variation of inhalation dose of pollutants. At typical running areas, the concentrations of CO, NO2, and PM2.5 were relatively higher in winter, whereas the concentrations of O3 were higher in spring and summer. The concentrations of CO, NO2, and PM2.5 were lower in the afternoon (16:00-18:00), whereas the concentrations of O3 were lower in the morning (06:00-08:00) and evening (18:00-20:00). There was a linear correlation between concentrations roadside to those in nearby parks, as the concentrations of CO roadside were generally consistent with those in parks (croad/cpark=1.01, R2=0.93), the concentrations of NO2 and PM2.5 roadside were higher than those in parks (croad/cpark were 0.56 and 1.19, respectively), and the concentrations of O3 roadside were lower than those in parks (croad/cpark=0.74, R2=0.97). During moderate or higher pollution conditions, 92% of joggers halted outdoor activities, 62.7% chose to jog in parks, 66.7% chose to jog at night, and 64.7% of joggers had single run mileages of 10-20 km. When people chose to jog in the afternoon and evening, the inhalation of CO, NO2, and PM2.5 were relatively lower, whereas the inhalation of O3 was higher. During spring and summer, night running after 20:00 reduced the O3 inhalation dose. Running roadside led to higher inhalation of CO, NO2, and PM2.5 than that in parks; however, O3 inhalation was lower.