Meteorologically normalized spatial and temporal variations investigation using a machine learning-random forest model in criteria pollutants across Tehran, Iran

COVID-19 mitigation measures and nitrogen dioxide – A quasi-experimental study of air quality in Munich, Germany

Interpretation of particle number size distribution, ultrafine particles and black carbon concentrations in Istanbul.

Contributions of carbonaceous particles from fossil emissions and biomass burning to PM10 in the Ruhr area, Germany

PM2.5 is an air pollution metric widely used to assess air quality, with the European Union having set targets for reduction in PM2.5 levels and population exposure. A major challenge for the scientific community is to identify, quantify and characterize the sources of atmospheric particles in the aspect of proposing effective control strategies. In the frame of ICARUS EU2020 project, a comprehensive database including PM2.5 concentration and chemical composition (ions, metals, organic/elemental carbon, Polycyclic Aromatic Hydrocarbons) from three sites (traffic, urban background, rural) of five European cities (Athens, Brno, Ljubljana, Madrid, Thessaloniki) was created. The common and synchronous sampling (two seasons involved) and analysis procedure offered the prospect of a harmonized Positive Matrix Factorization model approach, with the scope of identifying the similarities and differences of PM2.5 key-source chemical fingerprints across the sampling sites. The results indicated that the average contribution of traffic exhausts to PM2.5 concentration was 23.3% (traffic sites), 13.3% (urban background sites) and 8.8% (rural sites). The average contribution of traffic non-exhausts was 12.6% (traffic), 13.5% (urban background) and 6.1% (rural sites). The contribution of fuel oil combustion was 3.8% at traffic, 11.6% at urban background and 18.7% at rural sites. Biomass burning contribution was 22% at traffic sites, 30% at urban background sites and 28% at rural sites. Regarding soil dust, the average contribution was 5% and 8% at traffic and urban background sites respectively and 16% at rural sites. Sea salt contribution was low (1–4%) while secondary aerosols corresponded to the 16–34% of PM2.5. The homogeneity of the chemical profiles as well as their relationship with prevailing meteorological parameters were investigated. The results showed that fuel oil combustion, traffic non-exhausts and soil dust profiles are considered as dissimilar while biomass burning, sea salt and traffic exhaust can be characterized as relatively homogenous among the sites.

Source apportionment of ultrafine particles in urban Europe

Abstract. The COVID-19 lockdown measures gradually implemented in Lombardy (northern Italy) from 23 February 2020 led to a downturn in several economic sectors with possible impacts on air quality. Several communications claimed in the first weeks of March 2020 that the mitigation in air pollution observed at that time was actually related to these lockdown measures without considering that seasonal variations in emissions and meteorology also influence air quality. To determine the specific impact of lockdown measures on air quality in northern Italy, we compared observations from the European Commission Atmospheric Observatory of Ispra (regional background) and from the regional environmental protection agency (ARPA) air monitoring stations in the Milan conurbation (urban background) with expected values for these observations using two different approaches. On the one hand, intensive aerosol variables determined from specific aerosol characterisation observations performed in Ispra were compared to their 3-year averages. On the other hand, ground-level measured concentrations of atmospheric pollutants (NO2, PM10, O3, NO, SO2) were compared to expected concentrations derived from the Copernicus Atmosphere Monitoring Service Regional (CAMS) ensemble model forecasts, which did not account for lockdown measures. From these comparisons, we show that NO2 concentrations decreased as a consequence of the lockdown by −30 % and −40 % on average at the urban and regional background sites, respectively. Unlike NO2, PM10 concentrations were not significantly affected by lockdown measures. This could be due to any decreases in PM10 (and PM10 precursors) emissions from traffic being compensated for by increases in emissions from domestic heating and/or from changes in the secondary aerosol formation regime resulting from the lockdown measures. The implementation of the lockdown measures also led to an increase in the highest O3 concentrations at both the urban and regional background sites resulting from reduced titration of O3 by NO. The relaxation of the lockdown measures beginning in May resulted in close-to-expected NO2 concentrations in the urban background and to significant increases in PM10 in comparison to expected concentrations at both regional and urban background sites.

Abstract. East African countries face an increasing threat from poor air quality stemming from rapid urbanization, population growth, and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low-cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an AlphaSense OPC-N2 ran with a Raspberry Pi low-cost microcomputer, packaged in a weather-proof box. Measurements were conducted over a 2-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting 2 weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8±2.0 % in the fine particle fraction (PM2.5). The low-cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi.The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1 and 8.8 µg m−3, respectively. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8 and 13.0 µg m−3, respectively. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0 and 19.5 µg m−3, respectively. The urban measurements in Nairobi showed that PM concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow-type approach we can estimate the urban and roadside increments that are applicable to Nairobi (Lenschow et al., 2001). The median urban increment is 33.1 µg m−3 and the median roadside increment is 43.3 µg m−3 for PM2.5. For PM1, the median urban increment is 4.7 µg m−3 and the median roadside increment is 12.6 µg m−3. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi.A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high roadside increment indicating that vehicular traffic is a dominant source of PM in the city, accounting for approximately 48.1 %, 47.5 % and 57.2 % of the total PM loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low-cost sensors for PM measurement without gravimetric calibration available at all sites are discussed.This study shows that calibrated low-cost sensors can be successfully used to measure air pollution in cities like Nairobi. It demonstrates that low-cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.

Ultrafine particles (UFPs) can penetrate deeper into the respiratory system and cause more adverse health effects than particulate matter of a larger size. Therefore, their measurement should be promoted and not only episodically. Within the framework of the PM-Lab project, Belgian, Dutch and German partners from the Meuse-Rhine Euregion joined their efforts to investigate this topic. The present study describes, on one hand, the technological choices together with the set up requirements to include such a system within an air quality network, and on the other hand, the first results of our different mobile campaigns. As the monitoring of UFPs is neither regulated by a European directive nor normalized yet, major differences exist between the systems available on the market; our choices are discussed. The sampling strategy consisted in a long-term monitoring at a rural background station in Vielsalm (Belgium), along with several short-term campaigns led in different locations, a mixed urban background and traffic site in Herstal (Belgium), a traffic site in Maastricht (The Netherlands) and an urban background site in Mulheim (Germany). Corresponding analyses and results are described with a focus on the differences appearing between rural and urban sites, mainly in terms of time and size distributions.

Particulate matter and gaseous pollutants in the Mediterranean Basin: Results from the MED-PARTICLES project

Spatial Distribution of Nitrogen Oxides and Particulate Matter Concentrations in Taipei

Background: In Munich, Germany, a low emission zone (LEZ) and transit bans for heavy-duty vehicles have been introduced in 2008 to improve air quality and to meet policy targets. Aim: We investigated the effects of those measures on PM10 (particulate matter < 10 µm) mass concentration levels in Munich. Methods: In our analysis we compared PM10 concentrations measured prior to the implementation of any air quality measures (January 06 – January 08) with the PM10 levels measured after the measures became effective (October 08 – June 10). A semiparametric regression model with a first-order autocorrelated error term was applied for modeling hourly PM10 concentrations depending on the time of day, day of the week, the presence of the measures, background pollution, public holidays and wind direction. The changes in PM10 were quantified relative to a reference station representing regional background concentrations. Results: The mean PM10 concentration decreased at the traffic site from 30.0 µg/m3 prior to the measures to 26.0 µg/m3 thereafter, and at the background site from 27.3 µg/m3 to 25.0 µg/m3. The adjusted reduction was 13.8% at the traffic site and 4.8% at the urban background monitoring site. The effect was stronger in the summer season at the traffic site, whereas this was not observed at the background site. The effect sizes were dependent on time of day and day of the week. Conclusion: Traffic regulating measures aiming at the improvement of ambient air quality in Munich resulted in a decrease of ambient PM10 concentrations. The observed changes were larger at a traffic site than at an urban background site. The magnitude of this effect was in the range predicted by means of dispersion modeling for Munich. As dispersion modeling predicted even higher decrease of black smoke concentrations, we concluded that the introduced measures have more significant effects on human health than was anticipated when considering only the reduction of PM10 mass concentrations.

Anthropogenic activities in cities can be major sources of fine particulate matter which contribute significantly to increased mortality and disease. In rapidly developing cities of eastern Africa, lack of routine air pollution measurements have hampered formulation of actionable air quality policies. This study integrates ground-based observations of low-cost sensors (LCS) and regional chemical transport modelling (CHIMERE, https://www.lmd.polytechnique.fr/chimere/) to quantify spatial-temporal variability of PM2.5 and NO2 concentrations, primary/secondary aerosol loading, local versus regional pollution share, and  contribution of key economic sectors. Prior to deployment, LCS PM2.5 mass concentrations were calibrated with a reference instrument (BAM-1020), while LCS NO2 measurements could only be normalized internally. Between June-December 2021 period, sensors were deployed at urban background site (IPA, and UoN), urban traffic sites (KUCC, BuruBuru, and Marurui), and a peri-urban site (Ngong). BuruBuru and Marurui are in addition exposed to nearby residential emissions. Daily average PM2.5 varied from 26.3 to 27.6 µg/𝑚3 at traffic sites, 17.8 to 21.7 µg/𝑚3 at urban background sites,  while it was 20.3 µg/𝑚3 at peri-urban site. PM2.5 and NO2 diurnal patterns mimicked daily traffic cycle with constantly higher evening peaks compared to morning peaks indicating residential emissions. A link of  “large pollution” events with PM2.5 concentrations above 50 µg/m3 and low wind speeds (<4 m/s) was made evident and points to local sources. Preliminary modelling results of a nested CHIMERE run over Eastern Africa down to 2 km horizontal resolution show satisfying results when compared to measurements. They point to a strong urban source of fine particle pollution, with the strongest mass contribution of primary organic aerosol. Analysis of final model output will help to better understand air quality dynamics in Nairobi and ultimately help evaluation of possible future emission mitigation scenarios.

Abstract. The impact of road dust emissions on PM10 and PM2.5 (atmospheric particulate matter with diameteer < 10 μm and 2.5 μm mass concentrations recorded from 2003 to 2010 at 11 locations (rural, urban and industrial) in southern Spain was estimated based on the chemical characterization of PM and the use of a constrained Positive Matrix Factorization, where the chemical profile of local road dust samples is used as a priori knowledge. Results indicate that road dust increased PM10 levels on average by 21–35% at traffic sites, 29–34% at urban background sites heavily affected by road traffic emissions, 17–22% at urban-industrial sites and 9–22% at rural sites. Road dust contributions to ambient PM levels show a marked seasonality with maxima in summer and minima in winter, likely due to the rainfall frequency. Decreasing concentration trends over the sampling years were found at some traffic and urban sites but in most cases the decreases were less significant than for vehicle exhaust emissions, while concentrations increased at industrial sites, probably due to local peculiarities. Concerning PM2.5, road dust contributions were lower than in PM10, as expected but still important (21–31%, 11–31%, 6–16% and 7% for traffic, urban background, urban-industrial and rural sites, respectively). In addition the three main sources of road dust (carbonaceous particles, brake wear and road wear/mineral) were identified and their contributions to road dust mass loadings estimated, supporting the idea that air quality managers should drive measures aimed at preventing the build-up of road dust particles on roads.

London, like many major cities, has a noted air pollution problem, and a better understanding of the sources of airborne particles in the different size fractions will facilitate the implementation and effectiveness of control strategies to reduce air pollution. Thus, the trace elemental composition of the fine and coarse fraction were analysed at hourly time resolution at urban background (North Kensington, NK) and roadside (Marylebone Road, MR) sites within central London. Unlike previous work, the current study focuses on measurements during the summer providing a snapshot of contributing sources, utilising the high time resolution to improve source identification. Roadside enrichment was observed for a large number of elements associated with traffic emissions (Al, S, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Rb and Zr), while those elements that are typically from more regional sources (e.g. Na, Cl, S and K) were not found to have an appreciable increment. Positive Matrix Factorization (PMF) was applied for the source apportionment of the particle mass at both sites with similar sources being identified, including sea salt, airborne soil, traffic emissions, secondary inorganic aerosols and a Zn-Pb source. In the fine fraction, traffic emissions was the largest contributing source at MR (31.9%), whereas it was incorporated within an “urban background” source at NK, which had contributions from wood smoke, vehicle emissions and secondary particles. Regional sources were the major contributors to the coarse fraction at both sites. Secondary inorganic aerosols (which contained influences from shipping emissions and coal combustion) source factors accounted for around 33% of the PM10 at NK and were found to have the highest contributions from regional sources, including from the European mainland. Exhaust and non-exhaust sources both contribute appreciably to PM10 levels at the MR site, highlighting the continuing importance of vehicle-related air pollutants at roadside.

Evolution of air pollution source contributions over one decade, derived by PM10 and PM2.5 source apportionment in two metropolitan urban areas in Greece