Characteristics of anthropogenic heat with different modeling ideas and its driving effect on urban heat islands in seven typical Chinese cities.

Anthropogenic heat (AH), an essential urban heat source, is often overlooked or simplified in research on the multiple temporal and spatial driving mechanisms of the urban heat island (UHI), and case studies investigating the impacts of different AH connotations are scarce. This study estimated the AH in seven typical Chinese cities based on a remote sensing surface energy balance model (AHseb) and an energy consumption inventory-machine learning model (AHinv). The intensity of the surface UHI was extracted using land surface temperatures, and then the linear mixed-effects model and geographic detectors were used to analyze the driving effect of AH on the UHI. Despite the similar shapes of the spatial profile curves, the AH derived from the two models differed in both temporal and spatial characteristics, which was more typical in winter and in urban centers, and AHinv had a more notable central spread feature than AHseb. The AH driving effects on UHI were notably influenced by spatial and temporal heterogeneity, particularly in regions with distinct background climates. However, after controlling for the random effects of the background climate, AH still exhibited a considerable enhancing effect on the UHI. AHseb outperformed AHinv in terms of linear positive correlation and interpretation rate for UHI. Meanwhile, interactions with other potential factors enhanced AH driving effects. Consequently, UHI mitigation must be tailored to the local context by integrating multiple drivers, and for the heating effects of AH, it is necessary to develop specific mitigation measures by limiting the conversion of AHinv to AHseb in addition to reducing the heat production. The findings offer guidance for analyzing and optimizing urban thermal climates with a focus on AH or energy consumption control.

Anthropogenic heat variation during the COVID-19 pandemic control measures in four Chinese megacities.

Anthropogenic heat (AH) is an important input for the urban thermal environment. While reduction in AH during the Coronavirus disease 2019 (COVID-19) pandemic may have weakened urban heat islands (UHI), quantitative assessments on this are lacking. Here, a new AH estimation method based on a remote sensing surface energy balance (RS-SEB) without hysteresis from heat storage was proposed to clarify the effects of COVID-19 control measures on AH. To weaken the impact of shadows, a simple and novel calibration method was developed to estimate the SEB in multiple regions and periods. To overcome the hysteresis of AH caused by heat storage, RS-SEB was combined with an inventory-based model and thermal stability analysis framework. The resulting AH was consistent with the latest global AH dataset and had a much higher spatial resolution, providing objective and refined features of human activities during the pandemic. Our study of four Chinese megacities (Wuhan, Shanghai, Beijing, and Guangzhou) indicated that COVID-19 control measures severely restricted human activities and notably reduced AH. The reduction was up to 50% in Wuhan during the lockdown in February 2020 and gradually decreased after the lockdown was eased in April 2020, similar to that in Shanghai during the Level 1 pandemic response. In contrast, AH was less reduced in Guangzhou during the same period and increased in Beijing owing to extended central heating use in winter. AH decreased more in urban centers and the change in AH varied in terms of urban land use between cities and periods. Although UHI changes during the COVID-19 pandemic cannot be entirely attributed to AH changes, the considerable reduction in AH is an important feature accompanying the weakening of the UHI.

Spatial Distribution of PM2.5-Related Premature Mortality in China.

PM2.5 is a major component of air pollution in China and has a serious threat to public health. It is very important to quantify spatial characteristics of the health effects caused by outdoor PM2.5 exposure. This study analyzed the spatial distribution of PM2.5 concentration (45.9 µg/m3 national average in 2016) and premature mortality attributed to PM2.5 in cities at the prefectural level and above in China in 2016. Using the Global Exposure Mortality Model (GEMM), the total premature mortality in China was estimated to be 1.55 million persons, and the per capita mortality was 11.2 per 10,000 persons in the year 2016, resulting in higher estimates compared to the integrated exposure-response model. We assessed the premature mortality attributed to PM2.5 through common diseases, including ischemic heart disease (IHD), cerebrovascular disease (CEV), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and lower respiratory infections (LRI). The premature mortality due to IHD and CEV accounted for 68.5% of the total mortality, and the per capita mortality (per 10,000 persons) for all ages due to IHD was 3.86, the highest among diseases. For the spatial distribution of disease-specific premature mortality, the top two highest absolute numbers of premature mortality associated with IHD, CEV, LC, and LRI, respectively, were found in Chongqing and Beijing. In 338 cities of China, we have found a significant positive spatial autocorrelation of per capita premature mortality, indicating the necessity of coordinated regional governance for an efficient control of PM2.5.

Impact of urban heat island on meteorology and air quality at microenvironments.

This study analyzes the air pollution characteristics and their relation to meteorological conditions in Chennai, India. Meteorological conditions were the primary factor determining variations in daily average pollutant concentrations. The influence of urban infrastructure on meteorology is an important prediction on air quality. Understanding of the seasonal and diurnal secondary pollutant concentrations as a function of local meteorological conditions is necessary for urban air quality management. Micro-scale models for analyzing the surface layer interactions with the surrounding environment have recently gained attention. An attempt has been made to understand the effect of meteorology on air quality. This comprehensive study aims to assess the influence of local meteorology on urban air quality. The correlation was established between the change in meteorological parameters and mixing height on air quality at selected locations in a tropical urban environment. Results indicated the significant impact of land use patterns on the dispersion of air quality at study locations. Seasonal variations of ambient air temperatures at study locations were found to be more than 3°C in summer. Average mixing height variation among the study locations was observed to be more than 200 meters in summer. Results indicated the importance of wind velocity on the mixing height at study locations. The average concentrations of air quality parameters showed significant variation among the study locations. The maximum ozone (O3) concentration was recorded at the Central Business District (CBD) during the afternoon, i.e., around 38.3 ppb, whereas it was 26.8 and 14.6 ppb at the Residential Area (RA) and Urban Baseline (UBL), respectively. A strong correlation was observed between ambient temperature and O3 concentration during summer. In the winter, the average O3 concentration in all three-study locations increased to 45.3 ppb, 45.8 ppb, and 58.5 ppb at UBL, RA, and CBD sites, respectively. The study reveals the impact of microenvironments on air quality. Implications: An attempt has been made to study the seasonal and diurnal variation of air quality levels in selected study regions with land cover change. This article focuses mainly on the surface temperature intensity variations with respect to the percentage of land use pattern change in Chennai city, India, and the subsequent effect on meteorology of dispersion conditions and air quality parameters has been studied. The relationship between local meteorology and air quality has been established.

How to convert urban energy balance into contributions to urban excess temperatures?

A new approach partitioning the urban heat island intensity (ΔT) into its contributing processes is developed for the neighbourhood scale. The method transforms individual terms of the energy balance (radiation, evapotranspiration, heat storage, and convection) into partitions of temperature and is exemplified using the output of a micrometeorological model. •The temperature contribution is determined by climate sensitivity and a gain function depending on the energy redistribution factor.•The method is exemplified for the output of ENVI-met.•The software implementing the method is written in R language, a free language enabling statistical computations.

Wearable sensors for multifactorial personal exposure measurements - A ranking study.

Individuals are simultaneously exposed to multiple environmental stressors during their daily life. Studies of adverse health effects and their etiology as well as recommendations for a healthier life style demand for an assessment of multifactorial personal exposure, according to the exposome concept. A challenge is to record exposure while people are moving in heterogeneous urban environments. Therefore wearable sensor technologies are becoming a promising way to measure personal exposure continuously: indoors, outdoors and even on the move. So far, studies which test the accuracy and usability of wearable sensors for multiple stressors are lacking. Performance evaluations are important and should take place beforehand, especially to ensure the success of citizens-oriented studies. For the first time we rigorously examined the accuracy and application suitability of wearable sensors for acoustic noise, heat (temp), particle number counts (PNC) and geo-location (GPS) in different environments. We present an extensive device inter-comparison and a ranking of the sensors based on performance measures, Taylor diagrams, Bland-Altman plots, and ease-of-use aspects. The sensors showed moderate to high correlations with precision reference devices (r = 0.4-0.99). Differences between errors outdoors and indoors suggest that environmental conditions have impact upon the accuracy of the sensors. Reaction time, recording interval, and sensor ventilation are features that play a crucial role for both ease-of-use and accuracy. We conclude with a final performance () ranking: (GPS) >  (noise) >  (temp) >  (PNC). The results are relevant for future epidemiological studies of multifactorial exposure of individuals and their health and should guide the selection of wearables when persons are involved that are technically untaught. Inferences from multifactorial data are based on the performance of all sensors and the weakest chain links are PNC and temp sensors for which our article recommends urgent improvements.

Advanced Smartphone-Based Sensing with Open-Source Task Automation.

Smartphone-based sensing is becoming a convenient way to collect data in science, especially in environmental research. Recent studies that use smartphone sensing methods focus predominantly on single sensors that provide quantitative measurements. However, interdisciplinary projects call for study designs that connect both, quantitative and qualitative data gathered by smartphone sensors. Therefore, we present a novel open-source task automation solution and its evaluation in a personal exposure study with cyclists. We designed an automation script that advances the sensing process with regard to data collection, management and storage of acoustic noise, geolocation, light level, timestamp, and qualitative user perception. The benefits of this approach are highlighted based on data visualization and user handling evaluation. Even though the automation script is limited by the technical features of the smartphone and the quality of the sensor data, we conclude that task automation is a reliable and smart solution to integrate passive and active smartphone sensing methods that involve data processing and transfer. Such an application is a smart tool gathering data in population studies.

Degradation of indoor limonene by outdoor ozone: A cascade of secondary organic aerosols.

In indoor air, terpene-ozone reactions can form secondary organic aerosols (SOA) in a transient process. 'Real world' measurements conducted in a furnished room without air conditioning were modelled involving the indoor background of airborne particulate matter, outdoor ozone infiltrated by natural ventilation, repeated transient limonene evaporations, and different subsequent ventilation regimes. For the given setup, we disentangled the development of nucleated, coagulated, and condensed SOA fractions in the indoor air and calculated the time dependence of the aerosol mass fraction (AMF) by means of a process model. The AMF varied significantly between 0.3 and 5.0 and was influenced by the ozone limonene ratio and the background particles which existed prior to SOA formation. Both influencing factors determine whether nucleation or adsorption processes are preferred; condensation is strongly intensified by particulate background. The results provide evidence that SOA levels in natural indoor environments can surpass those known from chamber measurements. An indicator for the SOA forming potential of limonene was found to be limona ketone. Multiplying its concentration (in µg/m3) by 450(±100) provides an estimate of the concentration of the reacted limonene. This can be used to detect a high particle formation potential due to limonene pollution, e.g. in epidemiological studies considering adverse health effects of indoor air pollutants.

Urban heat island (UHI) influence on secondary pollutant formation in a tropical humid environment.

The combined action of urbanization (change in land use) and increase in vehicular emissions intensifies the urban heat island (UHI) effect in many cities in the developed countries. The urban warming (UHI) enhances heat-stress-related diseases and ozone (O3) levels due to a photochemical reaction. Even though UHI intensity depends on wind speed, wind direction, and solar flux, the thermodynamic properties of surface materials can accelerate the temperature profiles at the local scale. This mechanism modifies the atmospheric boundary layer (ABL) structure and mixing height in urban regions. These changes further deteriorate the local air quality. In this work, an attempt has been made to understand the interrelationship between air pollution and UHI intensity at selected urban areas located at tropical environment. The characteristics of ambient temperature profiles associated with land use changes in the different microenvironments of Chennai city were simulated using the Envi-Met model. The simulated surface 24-hr average air temperatures (11 m above the ground) for urban background and commercial and residential sites were found to be 30.81 ± 2.06, 31.51 ± 1.87, and 31.33 ± 2.1ºC, respectively. The diurnal variation of UHI intensity was determined by comparing the daytime average air temperatures to the diurnal air temperature for different wind velocity conditions. From the model simulations, we found that wind speed of 0.2 to 5 m/sec aggravates the UHI intensity. Further, the diurnal variation of mixing height was also estimated at the study locations. The estimated lowest mixing height at the residential area was found to be 60 m in the middle of night. During the same period, highest ozone (O3) concentrations were also recorded at the continuous ambient air quality monitoring station (CAAQMS) located at the residential area. IMPLICATIONS: An attempt has made to study the diurnal variation of secondary pollution levels in different study regions. This paper focuses mainly on the UHI intensity variations with respect to percentage of land use pattern change in Chennai city, India. The study simulated the area-based land use pattern with local mixing height variations. The relationship between UHI intensity and mixing height provides variations on local air quality.

Integrating modelling and smart sensors for environmental and human health.

Sensors are becoming ubiquitous in everyday life, generating data at an unprecedented rate and scale. However, models that assess impacts of human activities on environmental and human health, have typically been developed in contexts where data scarcity is the norm. Models are essential tools to understand processes, identify relationships, associations and causality, formalize stakeholder mental models, and to quantify the effects of prevention and interventions. They can help to explain data, as well as inform the deployment and location of sensors by identifying hotspots and areas of interest where data collection may achieve the best results. We identify a paradigm shift in how the integration of models and sensors can contribute to harnessing 'Big Data' and, more importantly, make the vital step from 'Big Data' to 'Big Information'. In this paper, we illustrate current developments and identify key research needs using human and environmental health challenges as an example.

The lasting effect of limonene-induced particle formation on air quality in a genuine indoor environment.

Atmospheric ozone-terpene reactions, which form secondary organic aerosol (SOA) particles, can affect indoor air quality when outdoor air mixes with indoor air during ventilation. This study, conducted in Leipzig, Germany, focused on limonene-induced particle formation in a genuine indoor environment (24 m(3)). Particle number, limonene and ozone concentrations were monitored during the whole experimental period. After manual ventilation for 30 min, during which indoor ozone levels reached up to 22.7 ppb, limonene was introduced into the room at concentrations of approximately 180 to 250 µg m(-3). We observed strong particle formation and growth within a diameter range of 9 to 50 nm under real-room conditions. Larger particles with diameters above 100 nm were less affected by limonene introduction. The total particle number concentrations (TPNCs) after limonene introduction clearly exceed outdoor values by a factor of 4.5 to 41 reaching maximum concentrations of up to 267,000 particles cm(-3). The formation strength was influenced by background particles, which attenuated the formation of new SOA with increasing concentration, and by ozone levels, an increase of which by 10 ppb will result in a six times higher TPNC. This study emphasizes indoor environments to be preferred locations for particle formation and growth after ventilation events. As a consequence, SOA formation can produce significantly higher amounts of particles than transported by ventilation into the indoor air.

Size-fractioned particulate air pollution and cardiovascular emergency room visits in Beijing, China.

BACKGROUND: Although short-term exposure to ambient particulate matter has increasingly been linked with cardiovascular diseases, it is not quite clear how physical characteristics of particles, such as particle size may be responsible for the association. This study aimed at investigating whether daily changes in number or mass concentrations of accurately size-segregated particles in the range of 3nm-10µm are associated with daily cardiovascular emergency room visits in Beijing, China. METHODS: Cardiovascular emergency room visit counts, particle size distribution data, and meteorological data were collected from Mar. 2004 to Dec. 2006. Particle size distribution data was used to calculate particle number concentration in different size fractions, which were then converted to particle mass concentration assuming spherical particles. We applied a time-series analysis approach. We evaluated lagged associations between cardiovascular emergency room visits and particulate number and mass concentration using distributed lag non-linear models up to lag 10. We calculated percentage changes of cardiovascular emergency room visits, together with 95% confidence intervals (CI), in association with an interquartile range (IQR, difference between the third and first quartile) increase of 11-day or 2-day moving average number or mass concentration of particulate matter within each size fraction, assuming linear effects. We put interaction terms between season and 11-day or 2-day average particulate concentration in the models to estimate the modification of the particle effects by season. RESULTS: We observed delayed associations between number concentration of ultrafine particles and cardiovascular emergency room visits, mainly from lag 4 to lag 10, mostly contributed by 10-30nm and 30-50nm particles. An IQR (9040cm(-3)) increase in 11-day average number concentration of ultrafine particles was associated with a 7.2% (1.1-13.7%) increase in total, and a 7.9% (0.5-15.9%) increase in severe cardiovascular emergency room visits. The delayed effects of particulate mass concentration were small. Regarding immediate effects, 2-day average number concentration of Aitken mode (30-100nm) particles had strongest effects. An IQR (2269cm(-3)) increase in 2-day average number concentration of 30-50nm particles led to a 2.4% (-1.5-6.5%) increase in total, and a 1.7% (-2.9-6.5%) increase in severe cardiovascular emergency room visits. The immediate effects of mass concentration came mainly from 1000-2500nm particles. An IQR (11.7µgm(-3)) increase in 2-day average mass concentration of 1000-2500nm particles led to an around 2.4% (0.4-4.4%) increase in total, and a 1.7% (-0.8-4.2%) increase in severe cardiovascular emergency room visits. The lagged effect curves of number and mass concentrations of 100-300nm particles or 300-1000nm particles were quite similar, indicating that using particulate number or mass concentrations seemed not to affect the cardiovascular effect (of particles within one size fraction). The effects of number concentration of ultrafine particles, sub-micrometer particles (3-1000nm) and 10-30nm particles were substantially higher in winter comparing with in summer. CONCLUSIONS: Elevated concentration levels of sub-micrometer particles were associated with increased cardiovascular morbidity. Ultrafine particles showed delayed effects, while accumulation mode (100-1000nm) particles showed immediate effects. Using number or mass concentrations did not affect the particle effects.

Genetic association between obstructive bronchitis and enzymes of oxidative stress.

OBJECTIVE: Obstructive respiratory diseases, mainly the chronic obstructive pulmonary disease (COPD) and asthma, are associated with functional polymorphisms of xenobiotic-metabolizing enzymes (XMEs). To date, association for obstructive bronchitis has not been described. MATERIAL/METHODS: In this study, we investigated the genotypes from 26 functional polymorphisms of 20 XMEs in children (n, 1028) at the age of 6 years from the German prospective birth cohort study (LISAplus) and analyzed the associations between genotypes and obstructive bronchitis. RESULTS: For the first time, we found noteworthy gene-disease associations for the functional PON1 M55L and EPHX1 H139R polymorphisms and gene-environment associations for the functional COMT V158M and NQO1 P187S polymorphisms after stratification for maternal active smoking behaviour during pregnancy. The noteworthy associations were substantiated by the biological findings that all the risk genotypes belong to genes involved in oxidative stress and code for proteins with a fast enzymatic activity or concomitantly appear in common estrogene-metabolizing pathway (COMT, NQO1). CONCLUSION: The oxidative stress has to be taken into account in mechanism of the obstructive bronchitis in early childhood. The risk genotypes may serve as risk factors for respiratory obstruction rather than for signs of COPD or asthma.

Associations between size-segregated particle number concentrations and respiratory mortality in Beijing, China.

Numerous studies have described the adverse associations between particle mass and respiratory health. The aim of the study was to analyze the associations of particle properties, especially size-segregated particle number concentrations (PNC), and respiratory mortality in Beijing, P.R. China. We gathered daily values of respiratory mortality and air pollution data of the Beijing urban area. Generalized additive models were used to estimate the associations. Single pollutant models showed that delayed concentrations of SO(2), total PNCs, and PNC of 300-1000 nm were adversely associated with total respiratory mortality. There was an indication that adverse health effects of PNCs might be stronger for stagnant air masses. Two-pollutant models verified the independence of associations of total PNCs of other pollutants (SO(2), NO(2), and PM(10)). In conclusion, particle number concentrations, especially accumulation mode particles, might be factors influencing the adverse associations between particulate matter and respiratory health.

Sub-micrometer particulate air pollution and cardiovascular mortality in Beijing, China.

BACKGROUND: While the link between particulate matter and cardiovascular mortality is well established, it is not fully investigated and understood which properties of the aerosol might be responsible for the health effects, especially in polluted mega-city areas. OBJECTIVES: Our goal was to explore the association between daily cardiovascular mortality and different particle metrics in the sub-micrometer range in Beijing, China. METHODS: We obtained daily counts of cause-specific cardiovascular deaths in the Beijing urban area for the period March 2004 to August 2005. Concurrently, continuous measurements of particle number size distributions were performed. Particle number concentrations (NC) between 0.003 µm and 0.8 µm were converted to particle mass and surface area concentrations assuming spherical particles. Semi-parametric Poisson regression models adjusting for trend, seasonality, day of the week, and meteorology were used to estimate immediate, delayed and cumulative particle effects. Additionally, effect modification by air mass origin was investigated. RESULTS: We observed associations between daily cardiovascular mortality and particle NC for a 2-days delay. Moreover, nearly all particle metrics showed 2-days delayed associations with ischemic heart disease mortality. The strongest association was found for particle NC in the size range 0.03-0.1 µm (7.1% increase in daily mortality with a 95%-confidence interval of 2.9%-11.5%, per an increase of 6250 particles/cm3). Results for surface and mass concentrations with a lag of two days indicated effect modification by air mass origin, whereas effects of particle NC were not modified. CONCLUSIONS: Results show an elevated risk of cardiovascular mortality in Beijing from short-term exposure to particulate air pollution in the sub-micrometer range. Results also indicate that locally produced smaller particles and regionally transported particles may exhibit different effects in Beijing.

Truncated Lévy flights and agenda-based mobility are useful for the assessment of personal human exposure.

Receptor-oriented approaches can assess the individual-specific exposure to air pollution. In such an individual-based model we analyse the impact of human mobility to the personal exposure that is perceived by individuals simulated in an exemplified urban area. The mobility models comprise random walk (reference point mobility, RPM), truncated Lévy flights (TLF), and agenda-based walk (RPMA). We describe and review the general concepts and provide an inter-comparison of these concepts. Stationary and ergodic behaviour are explained and applied as well as performance criteria for a comparative evaluation of the investigated algorithms. We find that none of the studied algorithm results in purely random trajectories. TLF and RPMA prove to be suitable for human mobility modelling, because they provide conditions for very individual-specific trajectories and exposure. Suggesting these models we demonstrate the plausibility of their results for exposure to air-borne benzene and the combined exposure to benzene and nonane.

Respiratory effects of indoor particles in young children are size dependent.

BACKGROUND: Extensive epidemiological studies have provided evidence of an association between elevated outdoor particulate air pollution and adverse health effects. However, while people typically spend majority of time indoors, there is limited knowledge on airborne indoor particles and on the correlation between the concentrations of indoor particles and health effects. Even insights into the influence of differently sized indoor particles on human health are still rare. OBJECTIVE: The association between differentially sized indoor air particles and the development of respiratory diseases was studied for three year aged children. METHODS: Short-term measurements of particle mass and number concentrations were carried out in children's rooms. Information on possible particle sources (smoking habits, type of heating, and traffic) and respiratory outcomes were obtained from questionnaires. Measured indoor particle concentrations were correlated with possible sources of indoor particles and with respiratory health impacts. RESULTS: Daily smoking, smoking more than 5 cigarettes per day at home and traffic density in front of the window of children's room were found to be related to indoor exposure by particles of different diameters. High indoor particle exposures were associated with an increased risk for the development of obstructive bronchitis and in some extent of non-obstructive bronchitis. The strongest impact was observed for the mass concentration of particles <1 µm and the number concentration of particles >0.5 µm. The risk increases still remain significant if tested for stability changing the number of adjustment variables or omitting randomly selected cases, respectively. CONCLUSION: Our results show significant associations between indoor particle concentrations and the risks for respiratory diseases in young children. The applied short-term measurements can help to assess the health risks of indoor particles with different sizes within epidemiological studies.

Size-segregated particle number concentrations and respiratory emergency room visits in Beijing, China.

BACKGROUND: The link between concentrations of particulate matter (PM) and respiratory morbidity has been investigated in numerous studies. OBJECTIVES: The aim of this study was to analyze the role of different particle size fractions with respect to respiratory health in Beijing, China. METHODS: Data on particle size distributions from 3 nm to 1 µm; PM10 (PM ≤ 10 µm), nitrogen dioxide (NO(2)), and sulfur dioxide concentrations; and meteorologic variables were collected daily from March 2004 to December 2006. Concurrently, daily counts of emergency room visits (ERV) for respiratory diseases were obtained from the Peking University Third Hospital. We estimated pollutant effects in single- and two-pollutant generalized additive models, controlling for meteorologic and other time-varying covariates. Time-delayed associations were estimated using polynomial distributed lag, cumulative effects, and single lag models. RESULTS: Associations of respiratory ERV with NO(2) concentrations and 100-1,000 nm particle number or surface area concentrations were of similar magnitude-that is, approximately 5% increase in respiratory ERV with an interquartile range increase in air pollution concentration. In general, particles < 50 nm were not positively associated with ERV, whereas particles 50-100 nm were adversely associated with respiratory ERV, both being fractions of ultrafine particles. Effect estimates from two-pollutant models were most consistent for NO(2). CONCLUSIONS: Present levels of air pollution in Beijing were adversely associated with respiratory ERV. NO(2) concentrations seemed to be a better surrogate for evaluating overall respiratory health effects of ambient air pollution than PM(10) or particle number concentrations in Beijing.

A rigorous small area modelling-study for the Helicobacter pylori epidemiology.

This paper presents an investigation into spatial risk differences over small distances for the Helicobacter pylori infection in the city of Leipzig, Germany and two rural districts. A model, using Bayesian inference, was developed that adjusts the risk for individual-specific factors, and for spatial or individual over-dispersion, respectively. Additionally, the model takes into account conditional spatial autocorrelation. We found a significant positive association to the H. pylori infection risk for: "more than three children live in the household" (OR=2.4, p=0.001), "more persons live per sq.m than average" (OR=1.4, p=0.03), "home situated at main road" (OR=1.4, p=0.04) and "using well water" (OR=2.3, p=0.05). A protective effect was identified for "travelled to low prevalence region" (OR=0.4, p<0.0001) and "born in Germany" (OR=0.2, p<0.0001). Three administrative areas with significantly increased spatial risk were identified: one in the rural district and two in the city of Leipzig. The model explained 24.9% of the total deviance. Contrary to expectations, the largest part of deviance of the data was not explained by the identified significant risk factors, but by individual-specific heterogeneities. We conclude that further - so far not discussed - factors influence the risk and the spatial variation of the H.pylori infection. Furthermore, from the results we speculate about a possible impact of long-time air pollution and surface water.