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Cyclists' exposure to air pollutants near roadways has been associated with numerous health effects. While the adverse health effects concerning aerosols have traditionally been assessed with data of particle mass concentrations, it appears that the number concentration is also another important indicator of toxicity. Thus, to holistically evaluate one's exposure to aerosol particles, assessments should be based on mass concentrations and number concentrations. In order to assess individual cyclists' exposure as they move through space and time, spatiotemporal high-resolution approaches are needed. Therefore, a mobile, fast-response monitoring platform was developed that uses a cargo bicycle as a base. Data of particle mass concentrations (PM1, PM2.5, PM10) and particle number concentrations (PN10) were collected along two different routes, one characterized by high-intensity vehicle traffic and one by low-intensity vehicle traffic. While high spatiotemporal heterogeneity was observed for all measured quantities, the PN10 concentrations fluctuated the most. High concentrations of PN10 could be clearly associated with vehicle traffic. For PM2.5, this relation was less pronounced. Mean particle concentrations of all measures were significantly higher along the high-traffic route. Comparing route exposures, the inhalation of PM2.5 was similar between both routes, whereas along the high-traffic route, cyclists were exposed to twice the particle number. We conclude that the cargo bike, featuring high-frequency mobile measurements, was useful to characterize the spatial distribution of mass concentrations and number concentrations across an urban environment. Overall, our results suggest that the choice of route is a key factor in reducing cyclists' exposure to air pollution.

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Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

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Human comfort in outdoor spaces (HCOS) is linked to people's psychological responses to environmental variables. Previous studies have established comfort ranges for these variables through interviews and questionnaires, reaching only limited populations. However, larger amounts of data would not only generate more robust results in local studies, but it would also allow for the possibility of creating an approach that could be applied to a wider range of weather conditions and different climates. Therefore, this study describes a new methodology to assess people's perception of weather based on human responses to weather conditions extracted from tweets, with the purpose of establishing comfort ranges for environmental variables. Tweets containing weather-associated keywords were collected, stored, and then linked to real-time meteorological data acquired nearby the locations in which the tweets were posted. Afterwards, people's perception of weather was extracted from the tweets using a classifier trained specifically on weather data that identified irrelevant, neutral, positive, and negative tweets. The obtained tweets and their related atmospheric data were analyzed to establish comfort ranges. The tweets' responses to effective temperature were very similar to those obtained in previous studies, although the peak of comfort is shifted towards the cold stress. Similarly, the tweets' responses to the thermohygrometric index were alike to previous results, but the peak of comfort is shifted towards the heat stress. Regarding the single weather variables under study, the obtained comfort ranges are similar to the ones found in previous research; in particular, the temperature comfort range matches perfectly at 20-22 °C. Therefore, it was concluded that tweets can be used to assess HCOS; not only are the results of this methodology comparable to results obtained in previous studies, but the procedure itself also shows new features and unexpected future applications.

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Long-term increased nutrient influx into normally nutrient-limited peatlands in combination with altered hydrological conditions may threaten a peatland's carbon storage function and affect its greenhouse gas (GHG) budget. However, in situ studies on the effects of long-term altered conditions on peatland functioning and GHG budgets are scarce. We thus quantified GHG fluxes in a peatland exposed to enhanced water level fluctuations and long-term nutrient infiltration in Ontario, Canada, via eddy-covariance and flux chamber measurements. The peatland was a prominent sink of - 680 ± 202 g carbon dioxide (CO2) and a source of 22 ± 8 g methane (CH4) m-2 year-1, resulting in a negative radiative forcing of - 80 g CO2 eq. m-2 y-1. During the growing season CH4 fluxes were constantly high (0.1 g m-2 s-1). Further, on three dates, we measured nitrous oxide (N2O) fluxes and observed a small flux of 2.2 mg m-2 day-1 occurring during the thawing period. Taking the studied ecosystem as a model system for other peatlands exposed to long-term increased nutrient infiltration and enhanced water level fluctuations, our data suggest that such peatlands can maintain their carbon storage function and CO2 sequestration may outweigh emissions of CH4.

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We conducted a 60-day roadside measurement campaign on a busy street in Münster, Germany, during summer 2016. We used gas and particle concentration measurements with high temporal resolution (10 Hz) to quantify both the emission ratios of nitrogen oxides per carbon dioxide (NO x /CO2) for over 70,000 individual exhaust plumes as well as the emission ratios for size-resolved particle numbers per carbon dioxide (d(PN CO2-1)/dlogD) for about 10,000 plumes. The real-world fleet passing by the measurement station consisted of passenger cars (85%), buses (5.9%), light duty commercial vehicles (5.7%), trucks (1.7%), and motorcycles (1.6%). The median measured NO x /CO2 ratio was 3.33 g kg-1. The median measured PN/CO2 emission ratio for particles with diameters between 0.03 and 10 µm was 5.6 × 1014 kg-1. We compared our results with the Handbook Emission Factors for Road Transport (HBEFA) and the Euro 5 and Euro 6 emission standards by employing traffic counts, assuming the diesel-to-gasoline ratios of vehicles according to registration statistics, and estimating that stop-and-go traffic occurred 65% of the time. Using a conservative estimate, our median ratios exceeded the HBEFA data by more than 65% for NO x /CO and by a factor of about 100 for PN/CO2. Furthermore, our median NO x emission per kilometer travelled (NO x  km-1) exceeded the Euro 5 emission limit for diesel cars by a factor of 3 and exceeded the Euro 6 limit by almost a factor of 7. Additionally, our median particle number emission (PN km-1) exceeded the Euro 5 and Euro 6 limits of diesel cars by a factor of almost 150. These results confirm the presumption that the emissions of a real-world traffic fleet comprehensively exceed the legal limits. Very likely, the widespread presence of defeat devices in vehicle emission control systems plays a major role in this discrepancy. This has a strong impact on the apparent inability of authorities to comply with the legal limits of the NO2 concentrations in urban air.

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The growing demand for agricultural products has been leading to an expansion and intensification of agriculture around the world. More and more unused land is currently reclaimed in the regions of the former Soviet Union. Driven by climate change, the Western Siberian grain belt might, in a long-term, even expand into the drained peatland areas to the North. It is crucial to study the consequences of this land-use change with respect to the carbon cycling as this is still a major knowledge gap. We present for the first time data on the atmosphere-ecosystem exchange of carbon dioxide and methane of an arable field and a neighboring unused grassland on peat soil in Western Siberia. Eddy covariance measurements were performed over one vegetation period. No directed methane fluxes were found due to an effective drainage of the study sites. The carbon dioxide fluxes appeared to be of high relevance for the global carbon and greenhouse gas cycles. They showed very site-specific patterns resulting from the development of vegetation: the persistent plants of the grassland were able to start photosynthesizing soon after snow melt, while the absence of vegetation on the managed field lead to a phase of emissions until the oat plants started to grow in June. The uptake peak of the oat field is much later than that of the grassland, but larger due to a rapid plant growth. Budgeting the whole measurement period, the grassland served as a carbon sink, whereas the oat field was identified to be a carbon source. The conversion from non-used grasslands on peat soil to cultivated fields in Western Siberia is therefore considered to have a positive feedback on climate change.

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High concentrations of airborne particulate matter (PM) have been associated with increased rates of morbidity and mortality among exposed populations. Although certain components of PM were suggested to influence these effects, no clear-cut correlation was determined thus far. One of the possible modes of action is the induction of oxidative stress by inhaled PM triggering inflammatory responses. Therefore, the in vitro formation of reactive oxygen species (ROS) in three cell lines in the presence of five subfractions of PM(10), collected in Münster, Germany was investigated. The PM components chloride, nitrate, ammonium, sulfate, 68 chemical elements, and endotoxin were quantified. The highest concentration of endotoxin was found in particles of 0.42-1.2 µm aerodynamic diameters, and therefore probably subject to long-range transport. Intracellular ROS formation in three well established mammalian cell lines (CaCo2, human; MDCK, canine; RAW264.7, mouse) only correlated positively with particle size. The two smallest PM size fractions provoked the highest rise in ROS. However, the latter did not correlate with the concentration of any PM components investigated. The smallest PM size fractions significantly dominated the number of particles. Therefore, the particle number may be most effective in inducing oxidative stress in vitro.

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The collection of fog water is a simple and sustainable technology to obtain fresh water for afforestation, gardening, and as a drinking water source for human and animal consumption. In regions where fresh water is sparse and fog frequently occurs, it is feasible to set up a passive mesh system for fog water collection. The mesh is directly exposed to the atmosphere, and the foggy air is pushed through the mesh by the wind. Fog droplets are deposited on the mesh, combine to form larger droplets, and run down passing into a storage tank. Fog water collection rates vary dramatically from site to site but yearly averages from 3 to 10 l m(-2) of mesh per day are typical of operational projects. The scope of this article is to review fog collection projects worldwide, to analyze factors of success, and to evaluate the prospects of this technology.

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In this study the performance of the American Meteorological Society and U.S. Environmental Protection Agency Regulatory Model (AERMOD), a Gaussian plume model, is compared in five test cases with the German Dispersion Model according to the Technical Instructions on Air Quality Control (Ausbreitungsmodell gemäbeta der Technischen Anleitung zur Reinhaltung der Luft) (AUSTAL2000), a Lagrangian model. The test cases include different source types, rural and urban conditions, flat and complex terrain. The predicted concentrations are analyzed and compared with field data. For evaluation, quantile-quantile plots were used. Further, a performance measure is applied that refers to the upper end of concentration levels because this is the concentration range of utmost importance and interest for regulatory purposes. AERMOD generally predicted concentrations closer to the field observations. AERMOD and AUSTAL2000 performed considerably better when they included the emitting power plant building, indicating that the downwash effect near a source is an important factor. Although AERMOD handled mountainous terrain well, AUSTAL2000 significantly underestimated the concentrations under these conditions. In the urban test case AUSTAL2000 did not perform satisfactorily. This may be because AUSTAL2000, in contrast to AERMOD, does not use any algorithm for nightly turbulence as caused by the urban heat island effect. Both models performed acceptable for a nonbuoyant volume source. AUSTAL2000 had difficulties in stable conditions, resulting in severe underpredictions. This analysis indicates that AERMOD is the stronger model compared with AUSTAL2000 in cases with complex and urban terrain. The reasons for that seem to be AUSTAL2000's simplification of the meteorological input parameters and imprecision because of rounding errors.

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BACKGROUND: Continuous monitoring of air quality is implemented by government institutions at fixed ambient sites. However, the correlation between fixed site measurements and exposure of individual persons to air contaminants is likely to be weak. MATERIALS AND METHODS: We measured particulate matter both outdoors and indoors by following the spatial movement of individuals. Sixteen test persons took part and carried a measurement backpack for a 24-h period. The backpack was comprised of a Grimm Aerosol Spectrometer model 1.109, a GPS device, and a video camera for tracking of human behavior. The spectrometer provided information about particle numbers and mass in 32-size classes with a high temporal resolution of 6 s. RESULTS: The personal exposure of individuals during 24 h could significantly exceed the outdoor particulate matter (PM)(10) concentrations measured at the fixed sites. The average 24-h exposure of all test persons for PM(10) varied from 27 to 322 µg m(-3). Environmental tobacco smoke and cooking emissions were among the main indoor sources for PM. The amount of particulate matter a test person was exposed to was highly dependent on the spatial behavior and the surrounding microenvironment conditions. DISCUSSION: Large-scale experiments including personal measurements might help to improve modeling approaches to approximate the actual exposure on a statistically sound basis.

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BACKGROUND: The Atacama Desert in Northern Chile is one of the most arid places on earth. However, fog occurs regularly at the coastal mountain range and can be collected at different sites in Chile to supply settlements at the coast with freshwater. This is also planned in the fog oasis Alto Patache (20°49'S, 70°09'W). For this pilot study, we collected fog water samples in July and August 2008 for chemical analysis to find indications for its suitability for domestic use. METHODS: Fog water samples were taken with a cylindrical scientific fog collector and from the net and the storage tank of a Large Fog Collector (LFC). RESULTS: The pHs of advective fog, originating from the stratus cloud deck over the Eastern Pacific, varied between 2.9 and 3.5. Orographic fog, which was formed locally, exhibited a pH of 2.5. About 50% of the total ionic concentration was due to sea salt. High percentages of sulfate and very high enrichment factors (versus sea salt) of heavy metals were found. Both backward trajectories and the enrichment factors indicate that the high concentrations of ions and heavy metals in fog were influenced by anthropogenic activities along the Chilean Pacific Coast such as power plants, mining, and steel industry. CONCLUSIONS: We found no direct indication for the importance of other sources such as the emission of dimethyl sulfide from the ocean and subsequent atmospheric oxidation for acidity and sulfate or soil erosion for heavy metal concentrations. When fog water was collected by the LFC, it apparently picked up large amounts of dry deposition which accumulated on the nets during fog-free periods. This material is rinsed off the collector shortly after the onset of a fog event with the water collected first. During the first flush, some concentrations of acidity, nitrate, As, and Se, largely exceeded the Chilean drinking water limits. Before any use of fog water for domestic purpose, its quality should be checked on a regular basis. Strategies to mitigate fog water pollution are given.

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The importance of street traffic and meteorological conditions on the concentrations of particulate matter (PM) with an aerodynamic diameter smaller than 10 microm (PM10) was studied in the city of Münster in northwest Germany. The database consisted of meteorological data, data of PM10 mass concentrations and fine particle number (6-225 nm diameter) concentrations, and traffic intensity data as counted with tally hand counters at a four- to six-lane road. On working days, a significant correlation could be found between the diurnal mean PM10 mass concentration and vehicle number. The lower number of heavy-duty vehicles compared with passenger cars contributed more to the particle number concentration on working days than on weekend days. On weekends, when the vehicle number was very low, the correlation between PM10 mass concentration and vehicle number changed completely. Other sources of PM and the meteorology dominated the PM concentration. Independent of the weekday, by decreasing the traffic by approximately 99% during late-night hours, the PM10 concentration was reduced by 12% of the daily mean value. A correlation between PM10 and the particle number concentration was found for each weekday. In this study, meteorological parameters, including the atmospheric stability of the boundary layer, were also accounted for. The authors deployed artificial neural networks to achieve more information on the influence of various meteorological parameters, traffic, and the day of the week. A multilayer perceptron network showed the best results for predicting the PM10 concentration, with the correlation coefficient being 0.72. The influence of relative humidity, temperature, and wind was strong, whereas the influence of atmospheric stability and the traffic parameters was weak. Although traffic contributes a constant amount of particles in a daily and weekly cycle, it is the meteorology that drives most of the variability.

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Impacts of individual behavior on personal exposure to particulate matter (PM) and the associated individual health effects are still not well understood. As outdoor PM concentrations exhibit highly temporal and spatial variations, personal PM exposure depends strongly on individual trajectories and activities. Furthermore, indoor environments deserve special attention due to the large fraction of the day people spend indoors. The indoor PM concentration in turn depends on infiltrated outdoor PM and indoor particle sources, partially caused by the activities of people indoor. We present an approach to estimate PM2.5 exposure levels for individuals based upon existing data sources and models. For this pilot study, six persons kept 24-hour diaries and GPS tracks for at least one working day and one weekend day, providing their daily activity profiles and the associated geographical locations. The survey took place in the city of Münster, Germany in the winter period between October 2006 and January 2007. Environmental PM2.5 exposure was estimated by using two different models for outdoor and indoor concentrations, respectively. For the outdoor distribution, a dispersion model was used and extended by actual ambient fixed site measurements. Indoor concentrations were modeled using a simple mass balance model with the estimated outdoor concentration fraction infiltrated and indoor activities estimated from the diaries. A limited number of three 24-hour indoor measurements series for PM were performed to test the model performance. The resulting average daily exposure of the 14 collected profiles ranged from 21 to 198 microg m(-3) and showed a high variability over the day as affected by personal behavior. Due to the large contribution of indoor particle sources, the mean 24-hour exposure was in most cases higher than the daily means of the respective outdoor fixed site monitors. This feasibility study is a first step towards a more comprehensive modeling approach for personal exposure, and therefore restricted to limited data resources. In future, this model framework not only could be of use for epidemiological research, but also of public interest. Any individual operating a GPS capable device may become able to obtain an estimate of its personal exposure along its trajectory in time and space. This could provide individuals a new insight into the influence of personal habits on their exposure to air pollution and may result in the adaptation of personal behavior to minimize risks.

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This paper describes remote monitoring of air pollutant emissions by a mobile lidar (light detection and ranging)/ sodar (sound detection and ranging) system. First, measurements are carried out in the flue gas plume of a public power plant. The investigations focus mainly on quantifying SO2 emissions, but the uncertainties of such measurements are also emphasized. Furthermore, an example providing valuable data sets for the development and validation of plume dispersion models is outlined with measurements of the dilution of SO2 along the plume axis. Series of repeated determinations of SO2 emissions show a large variation in the obtained flux values, with moderate margins of error. Incomplete recording of the plume within the individual lidar scans, induced by strong looping movements of the flue gas plume, predominantly causes the variations of flux values. Therefore, the highest flux values determined are considered to be the most exact. This is verified by a comparison of measured fluxes with in situ measurements made by the plant operators. The results further indicate that lidar measurements illustrate the location and dimension of aerosol plumes better than the location and dimension of the plumes of gaseous compounds. The wind direction affecting the plume at any moment can be determined faster by lidar than by sodar because the latter requires much longer time intervals of signal averaging. Measurements show higher concentrations of SO2 compared with results from a Gaussian plume model for periods of less than 5 min after dispersion. The findings emphasize the suitability of remote sensing for detecting emissions and for investigating the propagation and dilution of air pollutant plumes.

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Atmospheric concentrations of gaseous NH3 and HNO3 and of particulate NH4+ and NO3- were measured during various seasons at a forest ecosystem research site in the "Fichtelgebirge" mountains in Central Europe. Air masses arriving at this site were highly variable with respect to trace compound concentration levels and their concentration ratios. However, the distributions of NH4+ and NO3- within the aerosol particle size spectra exhibited some very consistent patterns, with the former dominating the fine particle concentrations, and the latter dominating the coarse particles range, respectively. Overall, the particulate phase (NH4+ + NO3-) dominated the atmospheric nitrogen budget (particulate and gas phase, NH4+ + NO3- + NH3 + HNO3) by more than 90% of the median total mixing ratio in winter, and by more than 60% in summer. The phase partitioning varied significantly between the winter and summer seasons, with higher relative importance of the gaseous species during summer, when air temperatures were higher and relative humidities lower as compared to the winter season. Reduced nitrogen dominated over oxidized nitrogen, indicating the prevailing influence of emissions from agricultural activity as compared to traffic emissions at this mountainous site. A model has been successfully applied in order to test the hypothesis of thermodynamic equilibrium between the particulate and gas phases.

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A ground-based Differential Absorption Lidar was employed to study the dynamics of atmospheric O3 within the planetary boundary layer of a basin in the 'Fichtelgebirge' mountains, NE Bavaria. In particular, the night-time dynamics of O3 linked to the ground were investigated. The Lidar system measured vertical profiles of O3 up to 1 km above ground. For detailed analysis of the night-time dynamics of ozone, supplementary data from three ground-based stations (measuring mixing ratios of O3 and NO(x), as well as meteorological parameters) are essential. The Lidar results could be evaluated with these data from various altitudes above the basin floor. For the station with the largest (vertical) distance to the ground-based Lidar, the agreement was very good at all times. The Lidar method proved to be useful for examining the spatial distribution of O3. The observed night-time decrease of O3 at the bottom of the basin was due to deposition and to advection of air masses containing less O3 from the mountain slopes.

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