Concentrations In Urban Environment Research Articles

Decomposing PM2.5 concentrations in urban environments into meaningful factors 2. Extracting the contribution of traffic-related exhaust emissions

Vehicle-emitted fine particulate matter (PM2.5) has been associated with significant health outcomes and environmental risks. This study estimates the contribution of traffic-related exhaust emissions (TREE) to observed PM2.5 using a novel factorization framework. Specifically, co-measured nitrogen oxides (NOx) concentrations served as a marker of vehicle-tailpipe emissions and were integrated into the optimization of a Non-negative Matrix Factorization (NMF) analysis to guide the factor extraction. The novel TREE-NMF approach was applied to long-term (2012–2019) PM2.5 observations from air quality monitoring (AQM) stations in two urban areas. The extracted TREE factor was evaluated against co-measured black carbon (BC) and PM2.5 species to which the TREE-NMF optimization was blind. The contribution of the TREE factor to the observed PM2.5 concentrations at an AQM station from the first location showed close agreement (R2=0.79) with monitored BC data. In the second location, a comparison of the extracted TREE factor with measurements at a nearby Surface PARTiculate mAtter Network (SPARTAN) station revealed moderate correlations with PM2.5 species commonly associated with fuel combustion, and a good linear regression fit with measured equivalent BC concentrations. The estimated concentrations of the TREE factor at the second location accounted for 7–11 % of the observed PM2.5 in the AQM stations. Moreover, analysis of specific days known to be characterized by little traffic emissions suggested that approximately 60–78 % of the traffic-related PM2.5 concentrations could be attributed to particulate traffic-exhaust emissions. The methodology applied in this study holds great potential in areas with limited monitoring of PM2.5 speciation, in particular BC, and its results could be valuable for both future environmental health research, regional radiative forcing estimates, and promulgation of tailored regulations for traffic-related air pollution abatement.

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

AbstractAir pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short‐term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short‐term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.

Quantifying and predicting air quality on different road types in urban environments using mobile monitoring and automated machine learning

Traffic emissions are a primary source of air pollution in urban areas, with air quality being influenced by different types of roads characterized by varying traffic volumes and speeds. Comprehending the distribution of air pollutants and the factors influencing it across different road types holds immense significance in endeavors to enhance air quality within urbanized regions. This study recorded concentrations of PM, SO2, NO2, CO, and O3 on different road types in Shenyang, China, using mobile monitoring. The impacts of road type and microclimatic factors on air quality were quantified using automated machine learning. Among the six road types, the suburban highway exhibited the highest PM, SO2, and NO2 pollution. On the other hand, secondary roads experienced the highest levels of CO and O3 pollution. The automated machine learning models provided accurate predictions for PM2.5, PM10, SO2, NO2, and O3 concentrations (R2 = 0.91, 0.83, 0.82, 0.83, 0.79, respectively). Relative humidity played the most significant role in PM2.5 and PM10 concentrations (55.93% and 59.39%, respectively), followed by air temperature (15.36% and 17.73%) and road types (14.28% and 8.74%). Road types contributed 24.33%, 20.60%, 16.61%, and 11.90% to SO2, CO, O3, and NO2 concentrations, respectively. Overall, this study addresses the limitations of previous research and provides a comprehensive understanding of the impact of road types on air pollutant concentrations in urban environments.

Particulate matter resuspension from simulated urban green floors using a wind tunnel-mounted closed chamber.

Green areas are thought to reduce particulate matter (PM) concentrations in urban environments. Plants are the key to PM reduction via various mechanisms, although most mechanisms do not lead to the complete removal of PM. Ultimately, PM falls into the soil via wind and rainfall. However, the fallen PM can re-entrain the atmosphere, which can affect plants capacity to reduce PM. In this study, we simulated an urban green floor and measured the resuspension of PM from the surface using a new experimental system, a wind tunnel-mounted closed chamber. The developed system is capable of quantifying the resuspension rate at the millimeter scale, which is measured by using the 1 mm node chain. This is adequate for simulating in situ green floors, including fallen branches and leaves. This addressed limitations from previous studies which focused on micrometer-scale surfaces. In this study, the surfaces consisted of three types: bare sand soil, broadleaves, and coniferous leaves. The resuspended PM was measured using a light-scattering dust detector. The resuspension rate was highest of 14.45×10-4 s-1 on broad-leaved surfaces and lowest on coniferous surfaces of 5.35×10-4 s-1 (p<0.05) and was not proportional to the millimeter-scale surface roughness measured by the roller chain method. This might be due to the lower roughness density of the broad-leaved surface, which can cause more turbulence for PM resuspension. Moreover, the size distribution of the resuspended PM indicated that the particles tended to agglomerate at 2.5 µm after resuspension. Our findings suggest that the management of fallen leaves on the urban green floor is important in controlling PM concentrations and that the coniferous floor is more effective than the broadleaved floor in reducing PM resuspension. Future studies using the new system can be expanded to derive PM management strategies by diversifying the PM types, surfaces, and atmospheric conditions.

Short-term effect of COVID-19 lockdowns on atmospheric CO2, CH4 and PM2.5 concentrations in urban environment.

The COVID-19 pandemic has changed all areas of human activity as it forced the authorities around the world to enact unprecedented restrictions such as “lockdowns”. The low economic activity reduced the anthropogenic impact on the environment, in particular, greenhouse gases and aerosols emissions were decreased. However, the associated change in air quality is difficult to directly observe and quantify, since concentrations of these components in urban areas are affected by many other factors. In this work statistical analysis of atmospheric CO2, CH4 and PM2.5, measured in 2017–2020 in the city of Ekaterinburg, Russia, are presented. A detailed focus was made on the lockdown period from March 28 to April 30, 2020. A significant decrease in concentrations and inter-hourly variations of all studied components were observed only in the short “self-isolation” period from April 6 to April 8. The anthropogenic origin of this effect, primarily associated with the reduction in vehicular traffic, was concluded from mean diurnal cycles and air temperature correlations of all components. A decrease in the difference between measured and background CO2 and CH4 mole fractions was also found during this period. The difference was 1.3±0.2 ppm for CO2 and 8±4 ppb for CH4, which was many times lower than during any other observed periods, suggesting a short-term effect of lockdown restrictions. Overall, a negative impact on the atmosphere quickly resumed after the recovery of economic activity. The approaches in this study can be used to detect weak fluctuations of atmospheric components in other urban territories.

Key determinants of particulate matter 2.5 concentrations in urban environments with scenario analysis

Particulate matter (PM) 2.5 generates a variety of negative effects on health, such as heart and lung disease, asthma, and respiratory symptoms. The pollutants in the atmosphere primarily result from human activities, and, in urban settings, increases in traffic volume and higher building density can elevate the level of PM2.5. Building on previous research, this study primarily focuses on two highly developed urban areas in the Texas Triangle region: Travis County in the Austin Metropolitan Area and Harris County in the Greater Houston Area. It explores different types of urban features, such as urban structures, land use/land cover, traffic volume, and distance from roads, that affect the PM2.5 concentration in urban environments at the local scale. Throughout this study, we use various research methods, including geographically weighted regression, to estimate the PM2.5 concentrations at local scales, 3D city models to derive urban characteristics, and the random forest algorithm to predict the effects of urban features on PM2.5 concentrations. Our findings suggest that developed land use, tall buildings in dense areas, and major traffic networks are the key contributors to PM2.5. However, we also find that tree canopy cover can significantly reduce PM2.5 concentrations.

Assessing the contamination level, sources and risk of potentially toxic elements in urban soil and dust of Iranian cities using secondary data of published literature.

Research in urban geochemistry has been expanding globally in recent years, following the trend of the ever-increasing human population living in cities. Environmental problems caused by non-degradable pollutants such as metals and metalloids are of particular interest considering the potential to affect the health of current and future urban residents. In comparison with the extensive global research on urban geochemistry, Iranian cities have not received sufficient study. However, rapid and often uncontrolled urban expansion in Iran over recent years has contributed to an increasing number of studies concerning contamination of urban soil and dust. The present work is based on a comprehensive nationwide evaluation and intercomparison of published quantitative datasets to determine the contamination levels of Iranian cities with respect to potentially toxic elements (PTEs) and assess health risks for urban population. Calculation of geoaccumulation, pollution, and integrated pollution indices facilitated the identification of the elements of most concern in the cities, while both carcinogenic and non-carcinogenic risks have been assessed using a widely accepted health-risk model. The analysis of secondary, literature data revealed a trend of contamination, particularly in old and industrial cities with some alarming levels of health risks. Among the elements of concern, As, Cd, Cu, and Pb were found to be most enriched in soils and dusts of the studied cities based on the calculated geochemical indices. The necessity of designing strategic plans to mitigate possible adverse effects of elevated PTE concentrations in urban environments is emphasized considering the role of long-term exposure in the occurrence of chronic carcinogenic and non-carcinogenic health problems.

Hibernation Conditions Contribute to the Differential Resistance to Cadmium between Urban and Forest Ant Colonies.

Simple SummaryThe resistance of organisms to trace metals can have a genetic or a plastic origin. Indeed, differential environmental conditions experienced before the exposure to trace metals could physiologically condition organisms and plastically enhance their subsequent resistance to trace metals. In this study on the ant Temnothorax nylanderi, we investigated whether the better cadmium resistance of urban colonies relative to forest colonies could originate from the distinct hibernation conditions that they experienced prior to cadmium exposure. We compared the ability of urban and forest colonies to resist cadmium depending on whether they had hibernated in their respective urban or forest habitats or under a laboratory common garden setup. We found that urban colonies resisted cadmium better than forest colonies when they had hibernated under a common garden. Surprisingly, this difference was not observed between urban and forest colonies that had hibernated in the field, in contrast with a previous study. One reason may be that winter was particularly mild on the year of our experiment. Our results therefore support the idea that urban colonies are genetically adapted to resist trace metals, but that this adaptation is only revealed under specific environmental conditions.Trace metals such as cadmium are found in high concentrations in urban environments. Animal and plant populations living in heavily contaminated environments could adapt to trace metals exposure. A recent study shows that urban populations of the acorn ant Temnothorax nylanderi are more resistant to cadmium than their forest counterparts. However, this study was performed using field colonies that had just come out of hibernation. Because urban and forest hibernation environments differ, the differential resistance to trace metals may originate either from differential hibernation conditions or from a different resistance baseline to cadmium. In this study, we tested these two hypotheses using laboratory common garden hibernation conditions. We let urban and forest colonies of the ant T. nylanderi hibernate under the same laboratory conditions for four months. After this hibernation period, we also collected field-hibernating colonies and we compared cadmium resistance between urban and forest colonies depending on the hibernation condition. We found a differential response to cadmium under common garden, with urban colonies displaying less larval mortality and lower size reduction of the produced individuals. This suggests a different resistance baseline of urban colonies to cadmium. However, unexpectedly, we did not detect the differential response between urban and forest colonies in the field, suggesting a more complex scenario involving both genetic and environmental influences.

Reprint of “Elemental evaluation of urban road dirt in tropical, arid and maritime climates”

Interest is in vehicular trace elemental contributions to the environment, evaluated via analysis of road dirt. Results from SEM/EDX, ICP-MS and ICP-OES are compared for a wide range of elements. ICP-OES and ICPMS provide absolute quantification while SEM/EDX has provided weight % assessments. Roadside dirt has been sampled along busy campus roads of universities/research centres in Guildford, Riyadh, Kuwait City, Doha and Kuala Lumpur, satisfying interest in climatic influences as well as vehicle engine size (Gulf non-commercial vehicle engine capacities tending to be greater than those in the UK or Malaysia). A total of 100 street dust samples have been analyzed, elemental concentrations being reported for Mg, Al, Ti, Mn, Fe, Co, Zr, Mo, Ba, Tl, V, Rd, Pb, Cu, Zn, Ni, Cr, Cd, As, Hg and Au. For all five urban centres, Al, Fe, Mg, and Si values were found to be well above background while concentrations of Ti, Cr and Ba were lower than values reported by others. Au and Ag from catalytic convertors were found in some centres, albeit at very low concentrations and with particle sizes of the order of 0.5 µm. The various techniques offer complementary information on vehicular emission elemental concentrations in urban environments.

Elemental evaluation of urban road dirt via three analytical techniques

Worldwide, toxicological consequences are arising from vehicular contributions to environmental pollution. Herein, elemental monitoring has been conducted of road dirt via three analytical techniques, sampling being made along the busy campus roads of universities/research institutes in Guildford and Riyadh. Using the ICP-OES and ICPMS techniques, absolute values have been determined for Ti, Mn, Mg, Co, Zr, Mo, Ba, Tl, V, Rd, Cu, Zn, Ni, Cr, Cd, and As, while an SEM/EDX technique has provided weight % values. Results, both qualitative and quantitative, are compared. By using SEM most prominent among the elements observed in the road samples are Al, Fe, Mg, and Si, particularly in the Gulf city of Riyadh and are in agreement with values observed in previous work by the same group. By using ICPMS and ICPOES Mg, Ti, V, Cu, Zn, Ni, Cr and Ba have also been observed in both climates, albeit at lower concentrations than that found for Al, Fe, and Mg, with particle sizes of < 45 μm. Elemental presence is seen to be influenced by climatic conditions as well as vehicle engine size. In conclusion, the various techniques offer complementary information on vehicular emission elemental concentrations in urban environments.

Simulating secondary organic aerosol in a regional air quality model using the statistical oxidation model – Part 3: Assessing the influence of semi-volatile and intermediate-volatility organic compounds and NO&amp;lt;sub&amp;gt;&amp;lt;i&amp;gt;x&amp;lt;/i&amp;gt;&amp;lt;/sub&amp;gt;

Abstract. Semi-volatile and intermediate-volatility organic compounds (SVOCs and IVOCs) from anthropogenic sources are likely to be important precursors of secondary organic aerosol (SOA) in urban airsheds, yet their treatment in most models is based on limited and obsolete data or completely missing. Additionally, gas-phase oxidation of organic precursors to form SOA is influenced by the presence of nitric oxide (NO), but this influence is poorly constrained in chemical transport models. In this work, we updated the organic aerosol model in the UCD/CIT (University of California at Davis/California Institute of Technology) chemical transport model to include (i) a semi-volatile and reactive treatment of primary organic aerosol (POA), (ii) emissions and SOA formation from IVOCs, (iii) the NOx influence on SOA formation, and (iv) SOA parameterizations for SVOCs and IVOCs that are corrected for vapor wall loss artifacts during chamber experiments. All updates were implemented in the statistical oxidation model (SOM) that simulates the oxidation chemistry, thermodynamics, and gas–particle partitioning of organic aerosol (OA). Model treatment of POA, SVOCs, and IVOCs was based on an interpretation of a comprehensive set of source measurements available up to the year 2016 and resolved broadly by source type. The NOx influence on SOA formation was calculated offline based on measured and modeled VOC:NOx ratios. Finally, the SOA formation from all organic precursors (including SVOCs and IVOCs) was modeled based on recently derived parameterizations that accounted for vapor wall loss artifacts in chamber experiments. The updated model was used to simulate a 2-week summer episode over southern California at a model resolution of 8 km. When combustion-related POA was treated as semi-volatile, modeled POA mass concentrations were reduced by 15 %–40 % in the urban areas in southern California but were still too high when compared against “hydrocarbon-like organic aerosol” factor measurements made at Riverside, CA, during the Study of Organic Aerosols at Riverside (SOAR-1) campaign of 2005. Treating all POA (except that from marine sources) to be semi-volatile, similar to diesel exhaust POA, resulted in a larger reduction in POA mass concentrations and allowed for a better model–measurement comparison at Riverside, but this scenario is unlikely to be realistic since this assumes that POA from sources such as road and construction dust are semi-volatile too. Model predictions suggested that both SVOCs (evaporated POA vapors) and IVOCs did not contribute as much as other anthropogenic precursors (e.g., alkanes, aromatics) to SOA mass concentrations in the urban areas (&lt; 5 % and &lt; 15 % of the total SOA respectively) as the timescales for SOA production appeared to be shorter than the timescales for transport out of the urban airshed. Comparisons of modeled IVOC concentrations with measurements of anthropogenic SOA precursors in southern California seemed to imply that IVOC emissions were underpredicted in our updated model by a factor of 2. Correcting for the vapor wall loss artifact in chamber experiments enhanced SOA mass concentrations although the enhancement was precursor-dependent as well as NOx-dependent. Accounting for the influence of NOx using the VOC:NOx ratios resulted in better predictions of OA mass concentrations in rural/remote environments but still underpredicted OA mass concentrations in urban environments. The updated model's performance against measurements combined with the results from the sensitivity simulations suggests that the OA mass concentrations in southern California are constrained within a factor of 2. Finally, simulations performed for the year 2035 showed that, despite reductions in VOC and NOx emissions in the future, SOA mass concentrations may be higher than in the year 2005, primarily from increased hydroxyl radical (OH) concentrations due to lower ambient NO2 concentrations.

Elemental evaluation of urban road dirt in tropical, arid and maritime climates

Interest is in vehicular trace elemental contributions to the environment, evaluated via analysis of road dirt. Results from SEM/EDX, ICP-MS and ICP-OES are compared for a wide range of elements. ICP-OES and ICPMS provide absolute quantification while SEM/EDX has provided weight % assessments. Roadside dirt has been sampled along busy campus roads of universities/research centres in Guildford, Riyadh, Kuwait City, Doha and Kuala Lumpur, satisfying interest in climatic influences as well as vehicle engine size (Gulf non-commercial vehicle engine capacities tending to be greater than those in the UK or Malaysia). A total of 100 street dust samples have been analyzed, elemental concentrations being reported for Mg, Al, Ti, Mn, Fe, Co, Zr, Mo, Ba, Tl, V, Rd, Pb, Cu, Zn, Ni, Cr, Cd, As, Hg and Au. For all five urban centres, Al, Fe, Mg, and Si values were found to be well above background while concentrations of Ti, Cr and Ba were lower than values reported by others. Au and Ag from catalytic convertors were found in some centres, albeit at very low concentrations and with particle sizes of the order of 0.5 µm. The various techniques offer complementary information on vehicular emission elemental concentrations in urban environments.

SUNSPACE, A Porous Material to Reduce Air Particulate Matter (PM).

The World Health Organization reports that every year several million people die prematurely due to air pollution. Poor air quality is a by-product of unsustainable policies in transportation, energy, industry, and waste management in the world's most crowded cities. Particulate matter (PM) is one of the major element of polluted air. PM can be composed by organic and inorganic species. In particular, heavy metals present in PM include, lead (Pb), mercury (Hg), cadmium, (Cd), zinc (Zn), nickel (Ni), arsenic (As), and molybdenum (Mo). Currently, vegetation is the only existing sustainable method to reduce anthropogenic PM concentrations in urban environments. In particular, the PM-retention ability of vegetation depends on the surface properties, related to the plant species, leaf and branch density, and leaf micromorphology. In this work, a new hybrid material called SUNSPACE (SUstaiNable materials Synthesized from by-Products and Alginates for Clean air and better Environment) is proposed for air PM entrapment. Candle burning tests are performed to compare SUNSPACE with Hedera Helix L. leafs with respect to their efficacy of reducing coarse and fine PM. The temporal variation of PM10 and PM2.5 in presence of the trapping materials, shows that Hedera Helix L. surface saturates more rapidly. In addition, the capability of SUNSPACE in ultrafine PM trapping is also demonstrated by using titanium dioxide nanoparticles with 25 nm diameter. Scanning electron microscope (SEM) and Transmission electron microscope (TEM) images of SUNSPACE after entrapment tests highlight the presence of collected nanoparticles until to about 0.04 mm in depth from the sample surface. N2 physisorption measurements allow to demonstrate the possibility to SUNSPACE regeneration by washing.

Temporal persistence of intra-urban spatial contrasts in ambient NO2, O3 and Ox in Edinburgh, UK

Ambient NO2, O3 and Ox (the sum of NO2 and O3) are associated with adverse health outcomes. Quantitative assessment of the health burden from these pollutants requires knowledge of small-scale variations in their concentrations in urban environments. In particular, we were interested in the temporal stability of intra-urban spatial contrasts in these pollutants. This was investigated by concurrent measurements of NO2 and O3 by passive samplers at 30 sites in Edinburgh, UK, repeated 12 × 1-weekly for NO2 and 6 × 2-weekly for O3 in summer and winter. Temporally persistent and large spatial variations in both NO2 and O3 concentrations were observed. Concentrations of NO2 across the sites ranged on average by a factor of 14 between suburban parks and heavily-trafficked roadsides, corresponding to a difference in NO2 on average of ∼80 μg m−3. Intra-urban O3 concentrations also varied substantially, on average by a factor of 4 (average range 45 μg m−3) and with strong anticorrelation to NO2 concentrations across the 30 sites. Consequently intra-urban variability was considerably lower for Ox than for NO2 and O3. The temporal stability in relative NO2 and O3 concentrations indicate potential for deriving intra-urban spatial fields of NO2 and O3 at different times by scaling models of long-term spatial patterns of NO2 and O3 by the measurements at a single site. If Ox is a key determinant of adverse health then the large intra-urban spatial contrasts in NO2 and O3 may be less relevant, with Ox concentrations across an urban area determined at a suitable background site.

A hybrid prediction model for PM2.5 mass and components using a chemical transport model and land use regression

GEOS-Chem, a chemical transport model, provides time-space continuous estimates of atmospheric pollutants including PM2.5 and its major components, but model predictions are not highly correlated with ground monitoring data. In addition, its spatial resolution is usually too coarse to characterize the spatial pattern in pollutant concentrations in urban environments. Our objective was to calibrate daily GEOS-Chem simulations using ground monitoring data and incorporating meteorological variables, land-use terms and spatial-temporal lagged terms. Major PM2.5 components of our interest include sulfate, nitrate, organic carbon, elemental carbon, ammonium, sea salt and dust. We used a backward propagation neural network to calibrate GEOS-Chem predictions with a spatial resolution of 0.500° × 0.667° using monitoring data collected during the period from 2001 to 2010 for the Northeastern United States. Subsequently, we made predictions at 1 km × 1 km grid cells. We determined the accuracy of the spatial-temporal predictions using ten-fold cross-validation and “leave-one-day-out” cross-validation techniques. We found a high total R2 for PM2.5 mass (all data R2 0.85, yearly values: 0.80–0.88) and PM2.5 components (R2 for individual components were around 0.70–0.80). Our model makes it possible to assess spatially- and temporally-resolved short- and long-term exposures to PM2.5 mass and components for epidemiological studies.

Wintertime particulate mass concentrations in urban environment and the impact of economic crisis

The aim of the specific study is to discuss the impact of economic crisis on air quality in Greece in terms of particulate matter (PM) concentrations. For this purpose, three sampling campaigns were conducted during the winter period of 2012, 2013 and 2014 in two medium sized cities in North Greece (Kavala and Drama). The average concentrations measured ranged from 33–56, 28–47 and 25–44 μg/m3 for PM10, PM2.5 and PM1, respectively. The analysis of the daily concentration profile for all measurements indicated two distinct periods of elevated concentrations: a) during 08:00 to 10:00 and b) during 19:00 to 22:00. The observed periods of increased concentration coincided with the periods of increased urban traffic in the morning and basic heating needs in late evening. Significant correlation was observed between PM10-PM2.5 (R2>0.9) and PM2.5-PM1 (R2∼1.0) suggesting that coarse and fine particles originate from similar sources. The PM2.5/PM10 ratio values ranged from 0.84 to 0.85 indicating a major impact of PM2.5 to the final concentration levels recorded. The results presented in the specific study support the notion that a significant alteration is undergoing to the atmospheric air quality in Greece due to the economic crisis and the subsequent increase of biomass products combustion for residential heating. Supplemental materials are available for this article.

Prediction of ultrafine particle number concentrations in urban environments by means of Gaussian process regression based on measurements of oxides of nitrogen

Gaussian process regression is used to predict ultrafine particle (UFP) number concentrations. We infer their number concentrations based on the concentrations of NO, NO2, CO and O3 at half hour and 5 min resolution. Because UFP number concentrations follow from a dynamic process, we have used a non-stationary kernel based on the addition of a linear and a rational quadratic kernel. Simultaneous measurements of UFP and gaseous pollutants were carried out during one month at three sampling locations situated within a 1 km2 area in a Belgian city, Antwerp. The method proposed provides accurate predictions when using NO and NO2 as covariates and less accurate predictions when using CO and O3. We have also evaluated the models for different training periods and we have found that a training period of at least seven days is suitable to let the models learn the UFP number concentration dynamics in different typologies of traffic.

Fish multigeneration test with preliminary short-term reproduction assay for estrone using Japanese medaka (Oryzias latipes).

The most potent chemicals potentially causing adverse effects on fish species are estrogens in human waste.Sewage is a source of these estrogens and it is difficult to reduce. In particular, although the bioactivity of estrone is estimated to be about half of that of estradiol, multiple studies report that more than 100 ng l(–1) of estrone can be detected in urban rivers, including discharges from sewage treatment works; approximately two times as high as estradiol. Few studies have been conducted to investigate the long-term effects of estrone on wildlife; therefore, we conducted fish multigeneration test using Japanese medaka (Oryzias latipes). Medaka were exposed to estrone for 27 weeks across three generations in environmentally relevant concentrations, being 5.74, 11.4, 24.0, 47.1 and 91.4 ng l(–1). No effects on reproduction were observed in the first generation; however, a decline in egg production and fertility was observed in the second generation exposed to 91.4 ng l(–1) estrone, which is lower than some known environmental concentrations in urban environments. Furthermore, histopathological abnormalities were observed in the third generation exposed to both 47.1 and 91.4 ng l(–1), suggesting that estrone possibly exerts severe effects on the third or later generations. However, appearances of testis–ova were observed in the second and third generation they were not consistent with actual effects on reproduction, notwithstanding the testis-ovais regarded as the key evidence for endocrine disruption. Accordingly, we consider that qualitative measurement of abnormalities using histopathological observations is required for appropriate evaluation of endocrine disruption.

Measuring and modeling particulate air pollution using a mobile, bicycle-based platform

Background: Air pollution concentrations exhibit significant within-city variability. Few studies use mobile measurements to estimate land-use regression models. Aim: To develop land-use regression models of particulate air pollution using a bicycle-based, mobile sampling method. Methods: We measured particulate air pollution during morning (7-9am; n=12 days) and afternoon (4-6pm; n=30 days) rush hours on weekdays (8/14 – 10/16/2012). Measurements were performed using a mobile platform. Our modified bicycle trailer contained equipment to measure: (1) particle number concentration, (2) PM2.5 mass concentration, (3) black carbon concentration, and (4) particle size distributions. Three sampling routes (~20 miles each) were cycled repeatedly (10 [4] times per route in the afternoon [morning]) to estimate typical rush hour concentrations in urban environments throughout Minneapolis, MN. Results: Our results are in mainly two categories: (1) correlates of the built environment and particulate air pollution and (2) land-use regression models to estimate concentrations of air pollution during rush hour for street segments in Minneapolis. We found particulate air pollution concentrations are related to aspects of the built environment, for example, mean particle number concentration varies by street classification: 19,009 pt/cc (arterials), 18,374 pt/cc (collectors), 14,059 pt/cc (local), and 13,186 pt/cc (off-street trails). In general, particle number concentrations were 1.9× higher during morning rush hour (mean: 31,043 pt/cc) than during afternoon rush hour (mean: 16,312 pt/cc). Policy implications: Our results demonstrate mobile-monitoring land-use regression, and inform efforts to reduce air pollution exposures and plan for health-promoting non-motorized transportation.

Methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments

A case study is presented to illustrate a methodology for mobile monitoring in urban environments. A dataset of UFP, PM2.5 and BC concentrations was collected. We showed that repeated mobile measurements could give insight in spatial variability of pollutants at different micro-environments in a city. Streets of contrasting traffic intensity showed increased concentrations by a factor 2–3 for UFP and BC and by <10% for PM2.5. The first quartile (P25) of the mobile measurements at an urban background zone seems to be good estimate of the urban background concentration. The local component of the pollutant concentrations was determined by background correction. The use of background correction reduced the number of runs needed to obtain representative results. The results presented, are a first attempt to establish a methodology for setup and data processing of mobile air quality measurements to assess the spatial variability of concentrations in urban environments.