Showing papers on "Air pollutant concentrations published in 2016"

AP-42, Compilation of Air Pollutant Emission Factors

Abstract: An emissions factor is a representative value that attempts to relate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant. These factors are usually expressed as the weight of pollutant divided by a unit weight, volume, distance, or duration of the activity emitting the pollutant (e.g., kilograms of particulate emitted per megagram of coal burned). Such factors facilitate estimation of emissions from various sources of air pollution. In most cases, these factors are simply averages of all available data of acceptable quality, and are generally assumed to be representative of long-term averages for all facilities in the source category (i.e., a population average).

The effect of future ambient air pollution on human premature mortality to 2100 using output from the ACCMIP model ensemble

Abstract: Ambient air pollution from ground-level ozone and fine particulate matter (PM2.5) is associated with premature mortality. Future concentrations of these air pollutants will be driven by natural and anthropogenic emissions and by climate change. Using anthropogenic and biomass burning emissions projected in the four Representative Concentration Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry-climate models simulated future concentrations of ozone and PM2.5 at selected decades between 2000 and 2100. We use output from the ACCMIP ensemble, together with projections of future population and baseline mortality rates, to quantify the human premature mortality impacts of future ambient air pollution. Future air pollution-related premature mortality in 2030, 2050 and 2100 is estimated for each scenario and for each model using a health impact function based on changes in concentrations of ozone and PM2.5 relative to 2000 and projected future population and baseline mortality rates. Additionally, the global mortality burden of ozone and PM2.5 in 2000 and each future period is estimated relative to 1850 concentrations, using present-day and future population and baseline mortality rates. The change in future ozone concentrations relative to 2000 is associated with excess global premature mortality in some scenarios/periods, particularly in RCP8.5 in 2100 (316 thousand deaths/year), likely driven by the large increase in methane emissions and by the net effect of climate change projected in this scenario, but it leads to considerable avoided premature mortality for the three other RCPs. However, the global mortality burden of ozone markedly increases from 382,000 (121,000 to 728,000) deaths/year in 2000 to between 1.09 and 2.36 million deaths/year in 2100, across RCPs, mostly due to the effect of increases in population and baseline mortality rates. PM2.5 concentrations decrease relative to 2000 in all scenarios, due to projected reductions in emissions, and are associated with avoided premature mortality, particularly in 2100: between -2.39 and -1.31 million deaths/year for the four RCPs. The global mortality burden of PM2.5 is estimated to decrease from 1.70 (1.30 to 2.10) million deaths/year in 2000 to between 0.95 and 1.55 million deaths/year in 2100 for the four RCPs, due to the combined effect of decreases in PM2.5 concentrations and changes in population and baseline mortality rates. Trends in future air pollution-related mortality vary regionally across scenarios, reflecting assumptions for economic growth and air pollution control specific to each RCP and region. Mortality estimates differ among chemistry-climate models due to differences in simulated pollutant concentrations, which is the greatest contributor to overall mortality uncertainty for most cases assessed here, supporting the use of model ensembles to characterize uncertainty. Increases in exposed population and baseline mortality rates of respiratory diseases magnify the impact on premature mortality of changes in future air pollutant concentrations and explain why the future global mortality burden of air pollution can exceed the current burden, even where air pollutant concentrations decrease.

Long-term Changes in Extreme Air Pollution Meteorology and the Implications for Air Quality.

Abstract: Extreme air pollution meteorological events, such as heat waves, temperature inversions and atmospheric stagnation episodes, can significantly affect air quality. Based on observational data, we have analyzed the long-term evolution of extreme air pollution meteorology on the global scale and their potential impacts on air quality, especially the high pollution episodes. We have identified significant increasing trends for the occurrences of extreme air pollution meteorological events in the past six decades, especially over the continental regions. Statistical analysis combining air quality data and meteorological data further indicates strong sensitivities of air quality (including both average air pollutant concentrations and high pollution episodes) to extreme meteorological events. For example, we find that in the United States the probability of severe ozone pollution when there are heat waves could be up to seven times of the average probability during summertime, while temperature inversions in wintertime could enhance the probability of severe particulate matter pollution by more than a factor of two. We have also identified significant seasonal and spatial variations in the sensitivity of air quality to extreme air pollution meteorology.

Method for Fusing Observational Data and Chemical Transport Model Simulations To Estimate Spatiotemporally Resolved Ambient Air Pollution

Abstract: Investigations of ambient air pollution health effects rely on complete and accurate spatiotemporal air pollutant estimates. Three methods are developed for fusing ambient monitor measurements and 12 km resolution chemical transport model (CMAQ) simulations to estimate daily air pollutant concentrations across Georgia. Temporal variance is determined by observations in one method, with the annual mean CMAQ field providing spatial structure. A second method involves scaling daily CMAQ simulated fields using mean observations to reduce bias. Finally, a weighted average of these results based on prediction of temporal variance provides optimized daily estimates for each 12 × 12 km grid. These methods were applied to daily metrics of 12 pollutants (CO, NO2, NOx, O3, SO2, PM10, PM2.5, and five PM2.5 components) over the state of Georgia for a seven-year period (2002–2008). Cross-validation demonstrates a wide range in optimized model performance across pollutants, with SO2 predicted most poorly due to limitati...

Air quality modelling in the Berlin-Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data

Abstract: Air pollution is the number one environmental cause of premature deaths in Europe. Despite extensive regulations, air pollution remains a challenge, especially in urban areas. For studying summertime air quality in the Berlin-Brandenburg region of Germany, the Weather Research and Forecasting Model with Chemistry (WRF-Chem) is set up and evaluated against meteorological and air quality observations from monitoring stations as well as from a field campaign conducted in 2014. The objective is to assess which resolution and level of detail in the input data is needed for simulating urban background air pollutant concentrations and their spatial distribution in the Berlin-Brandenburg area. The model setup includes three nested domains with horizontal resolutions of 15, 3 and 1 km and anthropogenic emissions from the TNO-MACC III inventory. We use RADM2 chemistry and the MADE/SORGAM aerosol scheme. Three sensitivity simulations are conducted updating input parameters to the single-layer urban canopy model based on structural data for Berlin, specifying land use classes on a sub-grid scale (mosaic option) and downscaling the original emissions to a resolution of ca. 1 km 1 km for Berlin based on proxy data including traffic density and population density. The results show that the model simulates meteorology well, though urban 2m temperature and urban wind speeds are biased high and nighttime mixing layer height is biased low in the base run with the settings described above. We show that the simulation of urban meteorology can be improved when specifying the input parameters to the urban model, and to a lesser extent when using the mosaic option. On average, ozone is simulated reasonably well, but maximum daily 8 h mean concentrations are underestimated, which is consistent with the results from previous modelling studies using the RADM2 chemical mechanism. Particulate matter is underestimated, which is partly due to an underestimation of secondary organic aerosols. NOx (NO+NO2) concentrations are simulated reasonably well on average, but nighttime concentrations are overestimated due to the model's underestimation of the mixing layer height, and urban daytime concentrations are underestimated. The daytime underestimation is improved when using downscaled, and thus locally higher emissions, suggesting that part of this bias is due to deficiencies in the emission input data and their resolution. The results further demonstrate that a horizontal resolution of 3 km improves the results and spatial representativeness of the model compared to a horizontal resolution of 15 km. With the input data (land use classes, emissions) at the level of detail of the base run of this study, we find that a horizontal resolution of 1 km does not improve the results compared to a resolution of 3 km. However, our results suggest that a 1 km horizontal model resolution could enable a detailed simulation of local pollution patterns in the Berlin-Brandenburg region if the urban land use classes, together with the respective input parameters to the urban canopy model, are specified with a higher level of detail and if urban emissions of higher spatial resolution are used. © 2016 Author(s).

Health Effects Of Transport Related Air Pollution

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High-resolution simulation of link-level vehicle emissions andconcentrations for air pollutants in a traffic-populated eastern Asian city

Abstract: . Vehicle emissions containing air pollutants created substantial environmental impacts on air quality for many traffic-populated cities in eastern Asia. A high-resolution emission inventory is a useful tool compared with traditional tools (e.g. registration data-based approach) to accurately evaluate real-world traffic dynamics and their environmental burden. In this study, Macau, one of the most populated cities in the world, is selected to demonstrate a high-resolution simulation of vehicular emissions and their contribution to air pollutant concentrations by coupling multimodels. First, traffic volumes by vehicle category on 47 typical roads were investigated during weekdays in 2010 and further applied in a networking demand simulation with the TransCAD model to establish hourly profiles of link-level vehicle counts. Local vehicle driving speed and vehicle age distribution data were also collected in Macau. Second, based on a localized vehicle emission model (e.g. the emission factor model for the Beijing vehicle fleet – Macau, EMBEV–Macau), this study established a link-based vehicle emission inventory in Macau with high resolution meshed in a temporal and spatial framework. Furthermore, we employed the AERMOD (AMS/EPA Regulatory Model) model to map concentrations of CO and primary PM2.5 contributed by local vehicle emissions during weekdays in November 2010. This study has discerned the strong impact of traffic flow dynamics on the temporal and spatial patterns of vehicle emissions, such as a geographic discrepancy of spatial allocation up to 26 % between THC and PM2.5 emissions owing to spatially heterogeneous vehicle-use intensity between motorcycles and diesel fleets. We also identified that the estimated CO2 emissions from gasoline vehicles agreed well with the statistical fuel consumption in Macau. Therefore, this paper provides a case study and a solid framework for developing high-resolution environment assessment tools for other vehicle-populated cities in eastern Asia.

Air Quality Strategies on Public Health and Health Equity in Europe—A Systematic Review

Abstract: Air pollution is an important public health problem in Europe and there is evidence that it exacerbates health inequities. This calls for effective strategies and targeted interventions. In this study, we conducted a systematic review to evaluate the effectiveness of strategies relating to air pollution control on public health and health equity in Europe. Three databases, Web of Science, PubMed, and Trials Register of Promoting Health Interventions (TRoPHI), were searched for scientific publications investigating the effectiveness of strategies on outdoor air pollution control, public health and health equity in Europe from 1995 to 2015. A total of 15 scientific papers were included in the review after screening 1626 articles. Four groups of strategy types, namely, general regulations on air quality control, road traffic related emission control interventions, energy generation related emission control interventions and greenhouse gas emission control interventions for climate change mitigation were identified. All of the strategies reviewed reported some improvement in air quality and subsequently in public health. The reduction of the air pollutant concentrations and the reported subsequent health benefits were more significant within the geographic areas affected by traffic related interventions. Among the various traffic related interventions, low emission zones appeared to be more effective in reducing ambient nitrogen dioxide (NO2) and particulate matter levels. Only few studies considered implications for health equity, three out of 15, and no consistent results were found indicating that these strategies could reduce health inequity associated with air pollution. Particulate matter (particularly fine particulate matter) and NO2 were the dominant outdoor air pollutants examined in the studies in Europe in recent years. Health benefits were gained either as a direct, intended objective or as a co-benefit from all of the strategies examined, but no consistent impact on health equity from the strategies was found. The strategy types aiming to control air pollution in Europe and the health impact assessment methodology were also discussed in this review.

Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

Abstract: Knowledge of the relationship between air quality and impact factors is very important for air pollution control and urban environment management. Relationships between winter air pollutant concentrations and local meteorological parameters, synoptic-scale circulations and precipitation were investigated based on observed pollutant concentrations, high-resolution meteorological data from the Weather Research and Forecast model and gridded reanalysis data. Artificial neural network (ANN) model was developed using a combination of numerical model derived meteorological variables and variables indicating emission and circulation type variations for estimating daily SO2, NO2, and PM10 concentrations over urban Lanzhou, Northwestern China. Results indicated that the developed ANN model can satisfactorily reproduce the pollution level and their day-to-day variations, with correlation coefficients between the modeled and the observed daily SO2, NO2, and PM10 ranging from 0.71 to 0.83. The effect of four factors, i.e., synoptic-scale circulation type, local meteorological condition, pollutant emission variation, and wet removal process, on the day-to-day variations of SO2, NO2, and PM10 was quantified for winters of 2002–2007. Overall, local meteorological condition is the main factor causing the day-to-day variations of pollutant concentrations, followed by synoptic-scale circulation type, emission variation, and wet removal process. With limited data, this work provides a simple and effective method to identify the main factors causing air pollution, which could be widely used in other urban areas and regions for urban planning or air quality management purposes.

Particulate Matter (PM) Pollution

Abstract: Particulate matter (PM) is one of the air pollutants regulated by the National Ambient Air Quality Standards (NAAQS). Reducing emissions of inhalable particles improves public health as well as visibility.

Using Low Cost Sensors to Measure Ambient Particulate Matter Concentrations and On-Road Emissions Factors

Abstract: . Air quality is a growing public concern in both developed and developing countries, as is the public interest in having information on air pollutant concentrations within their communities. Quantifying the spatial and temporal variability of ambient fine particulate matter (PM2.5) is of particular importance due to the well-defined health impacts associated with PM2.5. This work evaluates a number of select PM sensors (Shinyei: models PPD42NS, PPD20V, PPD60PV) under a variety of ambient conditions and locations including urban background and roadside sites in Atlanta, GA, as well as a location with substantially higher ambient concentrations in Hyderabad, India. Low cost sensor measurements were compared against reference monitors at all locations. On-road emissions factors were calculated at the Atlanta site by pairing PM2.5 and separately determined black carbon (BC) and carbon dioxide (CO2) measurements. On-road emission factors can vary in different locations and over time for a number of reasons, including vehicle fleet composition and driving patterns and behaviors, and current environmental policy. Emission factors can provide valuable information to inform researchers, citizens, and policy makers. The PPD20V sensors had the highest correlation with the reference environmental beta attenuation monitor (E-BAM) with R2 values above 0.80 at the India site while at the urban background site, the PPD60PV had the highest correlation with the tapered element oscillating microbalance (TEOM) with an R2 value of 0.30. At the roadside site, only the PPD20V was used, with an R2 value against the TEOM of 0.18. Emissions factors at the roadside site were calculated as 0.39 ± 0.10 g PM2.5 per kg fuel and 0.11 ± 0.01g BC per kg fuel, which compare well with other studies and estimates based on other instruments. The results of this work show the potential usefulness of these sensors for high concentration applications in developing countries and for their use in generating emissions factors.

Explaining road transport emissions: a non-technical guide

Abstract: Road transport is an important source of both greenhouse gases and air pollutants. Despite improvements in vehicle efficiencies over past decades, today the sector is responsible for almost one fifth of Europe's greenhouse gas emissions. Emissions from vehicles also lead to high concentrations of air pollutants above EU standards in many of Europe's cities. This report provides a summary of the current knowledge on vehicle emissions in Europe. It also explains how emissions are monitored and the common technologies used to limit them.

Air Pollution and Potential Health Risk in Ostrava Region - a Review

Abstract: Aim: The aim of this review was to collect all available data about air pollution in Ostrava, which is one of the most polluted area in central Europe and to make a concise assessment of health risks resulting from historical exposures of air pollutants since the beginning of the monitoring, i.e. since 1970 to the present time. Methods: All available information sources (the Czech Hydrometeorological Institute, the Institute of Public Health in Ostrava or publications) were used. To evaluate the exposures both short-term (hourly and daily) data and long term (yearly) data during 45 years were analysed. For health risk assessment the relationship between exposure and biological effects of pollutants published by the WHO and the US EPA were employed. Results: During the studied period annual average concentrations of PM10 ranged from 25 to 96 µg/m3; PM2.5 from 24 to 45 µg/m3; SO2 from 3.4 to 101.5 µg/m3; NO2 from 17.76 to 51.17 µg/m3; benzene from 0.24 to 9.2 µg/m3; benzo[a]pyrene from 2.1 to 14 ng/m3; arsenic from 1.2 to 9.5 ng/m3. Since the turn of the 80s and 90s of the 20th century trend of air pollutant concentrations has been decreasing until the turn of millennium, when it stopped, and it has been constant until present time. However, presented results demonstrate that the citizens of Ostrava have been exposed to relatively high concentrations of pollutants in comparison to other similar cities. The most significant pollutants contributing to health risks are airborne dust (PM10, PM2.5), benzene and benzo[a]pyrene. The long-term average health risk of PM10 has increased in case of postneonatal infant mortality up to 30%; prevalence of bronchitis in children up to 61%; and incidence of chronic bronchitis in adults up to 89%. The long-term average health risk of PM2.5 increased for all-cause mortality in persons aged 30+ years up to 22%; cardiopulmonary related mortality up to 25%; and lung cancer related mortality up to 39%. The highest carcinogenic risk is observed in benzo[a]pyrene, when the range of individual lifetime carcinogenic risk is up to 1.25*10-3. This assessment is valid according to the strict carcinogenic risk by the WHO, while the maximum carcinogenic risk according the US EPA is 7.2*10-5. Conclusions: A significant reduction of the pollutants' concentrations in Ostrava in the nineties of the last century does not mean a required improvement of outdoor air quality to the desired level. Persisting episodes with a very strong short-term increase of the concentration of PM10 and PM2.5, as well as long-term load of these substances on the population is very high. Health risks from such burdens are likely to lead to a higher mortality and morbidity especially from specific diseases.

Air toxics in Canada measured by the National Air Pollution Surveillance (NAPS) program and their relation to ambient air quality guidelines

Abstract: This study reports ambient concentrations of 63 air toxics that were measured in Canada by the National Air Pollution Surveillance (NAPS) program over the period 2009–2013. Measured concentrations are compared with ambient air quality guidelines from Canadian jurisdictions, and compounds that exceeded guidelines are identified and discussed. Although this study does not assess risk or cumulative effects, air toxics that approached guidelines are also identified so that their potential contribution to ambient air toxics pollution can be considered. Eleven air toxics exceeded at least one guideline, and an additional 16 approached guidelines during the study period. Four compounds were measured using methods whose detection limits exceeded a guideline value, three of which could not be compared with guidelines, since they were not detected in any samples. The assessment of several metal(loid) concentrations is tentative, since they were measured only in fine particulate matter (PM) but compared with...

Spatiotemporal patterns of particulate matter (PM) and associations between PM and mortality in Shenzhen, China

Abstract: Most studies on air pollution exposure and its associations with human health in China have focused on the heavily polluted industrial areas and/or mega-cities, and studies on cities with comparatively low air pollutant concentrations are still rare. Only a few studies have attempted to analyse particulate matter (PM) for the vibrant economic centre Shenzhen in the Pearl River Delta. So far no systematic investigation of PM spatiotemporal patterns in Shenzhen has been undertaken and the understanding of pollution exposure in urban agglomerations with comparatively low pollution is still limited.

Impact of climate change on air quality and public health in urban areas

Abstract: This review discusses how climate undergo changes and the effect of climate change on air quality as well as public health. It also covers the inter relationship between climate and air quality. The air quality discussed here are in relation to the 5 criteria pollutants; ozone (O3), carbon dioxide (CO2), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM). Urban air pollution is the main concern due to higher anthropogenic activities in urban areas. The implications on health are also discussed. Mitigating measures are presented with the final conclusion.

Urban Air Pollution in Taiwan before and after the Installation of a Mass Rapid Transit System.

Abstract: Urbanization causes air pollution in metropolitan areas, coupled with meteorological factors that affect air quality. Although previous studies focused on the relationships of urbanization, air pollution, and climate change in Western countries, this study evaluated long-term variations of air quality and meteorological factors in Taiwanese metropolitan areas (Taipei area, Taichung City, and Kaohsiung City) and a rural area (Hualien County) between 1993 and 2012. The influence of a mass rapid transit (MRT) system on air quality was also evaluated. Air pollutant concentrations and meteorology data were collected from Taiwan Environmental Protection Administration (TEPA) air monitoring stations and Central Weather Bureau stations in the surveyed areas, respectively. Analyses indicate that levels of air pollution in metropolitan areas were greater than in the rural area. Kaohsiung City had the highest levels of O, SO, and particulate matter 2.5 or 10 µm in diameter (PM and PM). Clear downward trends for CO, NO, PM, PM, and especially SO concentrations were found in the surveyed areas, whereas O showed no decrease. Both O and PM concentrations showed similar bimodal seasonal distributions. Taiwan's air quality has improved significantly since 1993, indicating the effectiveness of promoting air pollution strategies and policies by the TEPA. Air pollution had an obvious improvement in Taipei area after the MRT system began operations in 1996. Because global climate may potentially affect urban air pollution in Taiwan, further study to clarify the mechanisms by which air pollution may affect human health and other biological effects is warranted.

Using statistical models to explore ensemble uncertainty in climate impact studies: the example of air pollution in Europe

Abstract: . Because of its sensitivity to unfavorable weather patterns, air pollution is sensitive to climate change so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, the computing cost of such methods requires optimizing ensemble exploration techniques. By using a training data set from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for eight regions in Europe and developed statistical models that could be used to predict air pollutant concentrations. The evolution of the key climate variables driving either particulate or gaseous pollution allows selecting the members of the EuroCordex ensemble of regional climate projections that should be used in priority for future air quality projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM following the EuroCordex terminology). After having tested the validity of the statistical model in predictive mode, we can provide ranges of uncertainty attributed to the spread of the regional climate projection ensemble by the end of the century (2071–2100) for the RCP8.5. In the three regions where the statistical model of the impact of climate change on PM2.5 offers satisfactory performances, we find a climate benefit (a decrease of PM2.5 concentrations under future climate) of −1.08 (±0.21), −1.03 (±0.32), −0.83 (±0.14) µg m−3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the Iberian Peninsula and the Mediterranean, the statistical model is not considered skillful enough to draw any conclusion for PM2.5. In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern Italy, the statistical model of the impact of climate change on ozone was considered satisfactory and it confirms the climate penalty bearing upon ozone of 10.51 (±3.06), 11.70 (±3.63), 11.53 (±1.55), 9.86 (±4.41), 4.82 (±1.79) µg m−3, respectively. In the British-Irish Isles, Scandinavia and the Mediterranean, the skill of the statistical model was not considered robust enough to draw any conclusion for ozone pollution.

Can changing the timing of outdoor air intake reduce indoor concentrations of traffic-related pollutants in schools?

Abstract: Traffic emissions have been associated with a wide range of adverse health effects. Many schools are situated close to major roads, and as children spend much of their day in school, methods to reduce traffic-related air pollutant concentrations in the school environment are warranted. One promising method to reduce pollutant concentrations in schools is to alter the timing of the ventilation so that high ventilation time periods do not correspond to rush hour traffic. Health Canada, in collaboration with the Ottawa-Carleton District School Board, tested the effect of this action by collecting traffic-related air pollution data from four schools in Ottawa, Canada, during October and November 2013. A baseline and intervention period was assessed in each school. There were statistically significant (P < 0.05) reductions in concentrations of most of the pollutants measured at the two late-start (9 AM start) schools, after adjusting for outdoor concentrations and the absolute indoor-outdoor temperature difference. The intervention at the early-start (8 AM start) schools did not have significant reductions in pollutant concentrations. Based on these findings, changing the timing of the ventilation may be a cost-effective mechanism of reducing traffic-related pollutants in late-start schools located near major roads.

Impact of meteorological conditions on airborne fine particle composition and secondary pollutant characteristics in urban area during winter-time

Abstract: The assessment of airborne fine particle composition and secondary pollutant characteristics in the case of Augsburg, Germany, during winter (31 January–12 March 2010) is studied on the basis of aerosol mass spectrometry (3 non-refractory components and organic matter, 3 positive matrix factorizations (PMF) factors), particle size distributions (PSD, 5 size modes, 5 PMF factors), further air pollutant mass concentrations (7 gases and VOC, black carbon, PM10, PM2.5) and meteorological measurements, including mixing layer height (MLH), with one-hourly temporal resolution. Data were subjectively assigned to 10 temporal phases which are characterised by different meteorological influences and air pollutant concentrations. In each phase hierarchical clustering analysis with the Ward method was applied to the correlations of air pollutants, PM components, PM source contributions and PSD modes and correlations of these data with all meteorological parameters. This analysis resulted in different degrees of sensitivities of these air pollutant data to single meteorological parameters. It is generally found that wind speed (negatively), MLH (negatively), relative humidity (positively) and wind direction influence primary pollutant and accumulation mode particle (size range 100–500 nm) concentrations. Temperature (negatively), absolute humidity (negatively) and also relative humidity (positively) are relevant for secondary compounds of PM and particle (PM2.5, PM10) mass concentrations. NO, nucleation and Aitken mode particle and the fresh traffic aerosol concentrations are only weakly dependent on meteorological parameters and thus are driven by emissions. These daily variation data analyses provide new, detailed meteorological influences on air pollutant data with the focus on fine particle composition and secondary pollutant characteristics and can explain major parts of certain PM component and gaseous pollutant exposure.