David M. Broday

Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013.

Exposure to ambient air pollution is a major risk factor for global disease. Assessment of the impacts of air pollution on population health and evaluation of trends relative to other major risk factors requires regularly updated, accurate, spatially resolved exposure estimates. We combined satellite-based estimates, chemical transport model simulations, and ground measurements from 79 different countries to produce global estimates of annual average fine particle (PM2.5) and ozone concentrations at 0.1° × 0.1° spatial resolution for five-year intervals from 1990 to 2010 and the year 2013. These estimates were applied to assess population-weighted mean concentrations for 1990-2013 for each of 188 countries. In 2013, 87% of the worlds population lived in areas exceeding the World Health Organization Air Quality Guideline of 10 μg/m(3) PM2.5 (annual average). Between 1990 and 2013, global population-weighted PM2.5 increased by 20.4% driven by trends in South Asia, Southeast Asia, and China. Decreases in population-weighted mean concentrations of PM2.5 were evident in most high income countries. Population-weighted mean concentrations of ozone increased globally by 8.9% from 1990-2013 with increases in most countries-except for modest decreases in North America, parts of Europe, and several countries in Southeast Asia.

Growth and Deposition of Hygroscopic Particulate Matter in the Human Lungs

Transport and fate of inhaled particulate matter in the human lungs is calculated for realistic physicochemical conditions by a new dosimetry model. The model solves a variant of the general dynamic equation for the size evolution of respirable particles within the human tracheobronchial airways, starting at the tracheal entrance. We focus on ambient anthropogenic aerosols, which are of concern in inhalation toxicology because of their potential irritant and toxic effects on humans. The aerosols considered are polydisperse with respect to size and heterodisperse with respect to thermodynamic state and chemical composition, having initially bimodal lognormal size distribution that evolves with time as a result of condensation-evaporation and deposition processes. The architecture of the human lung is described by Weibels symmetric bronchial tree. Simulations reveal that, due to the rapid growth of submicron-sized particles, increased number and mass fractions of the particle population can be found in the intermediate size range 0.1 < φ < 1

On the feasibility of measuring urban air pollution by wireless distributed sensor networks

Accurate evaluation of air pollution on human-wellbeing requires high-resolution measurements. Standard air quality monitoring stations provide accurate pollution levels but due to their sparse distribution they cannot capture the highly resolved spatial variations within cities. Similarly, dedicated field campaigns can use tens of measurement devices and obtain highly dense spatial coverage but normally deployment has been limited to short periods of no more than few weeks. Nowadays, advances in communication and sensory technologies enable the deployment of dense grids of wireless distributed air monitoring nodes, yet their sensor ability to capture the spatiotemporal pollutant variability at the sub-neighborhood scale has never been thoroughly tested. This study reports ambient measurements of gaseous air pollutants by a network of six wireless multi-sensor miniature nodes that have been deployed in three urban sites, about 150 m apart. We demonstrate the networks capability to capture spatiotemporal concentration variations at an exceptional fine resolution but highlight the need for a frequent in-situ calibration to maintain the consistency of some sensors. Accordingly, a procedure for a field calibration is proposed and shown to improve the systems performance. Overall, our results support the compatibility of wireless distributed sensor networks for measuring urban air pollution at a sub-neighborhood spatial resolution, which suits the requirement for highly spatiotemporal resolved measurements at the breathing-height when assessing exposure to urban air pollution.

Revealing source signatures in ambient BTEX concentrations.

Management of ambient concentrations of Volatile Organic Compounds (VOCs) is essential for maintaining low ozone levels in urban areas where its formation is under a VOC-limited regime. The significant decrease in traffic-induced VOC emissions in many developed countries resulted in relatively comparable shares of traffic and non-traffic VOC emissions in urban airsheds. A key step for urban air quality management is allocating ambient VOC concentrations to their pertinent sources. This study presents an approach that can aid in identifying sources that contribute to observed BTEX concentrations in areas characterized by low BTEX concentrations, where traditional source apportionment techniques are not useful. Analysis of seasonal and diurnal variations of ambient BTEX concentrations from two monitoring stations located in distinct areas reveal the possibility to identify source categories. Specifically, the varying oxidation rates of airborne BTEX compounds are used to allocate contributions of traffic emissions and evaporative sources to observed BTEX concentrations.

Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.

Application of Cloud Dynamics to Dosimetry of Cigarette Smoke Particles in the Lungs

Clinical data suggest a relationship between in vivo deposition patterns of cigarette smoke particles and the occurrence of tumors in the lung. Traditional dosimetry models fail to predict the preferential proximal deposition of cigarette smoke in the human airways, which resembles deposition of aerosol with a larger mass median aerodynamic diameter (MMAD) than that representative of cigarette smoke. Previous work has shown that accounting for the so-called cloud effect leads to enhanced proximal deposition and to better agreement with clinical and experimental data. This work presents an improved model of transport and deposition of cigarette smoke in the airways of smokers, accounting for possible particle-particle interactions (cloud effect) and their effect on the mobility of individual particles and on the deposition profile. Brinkmans effective medium approach is used for modeling the flow through and around the cloud, with the clouds permeability changing according to the clouds solid volume fraction. Although the weakest of all interparticle hydrodynamic interactions is considered, it significantly alters the deposition pattern along the respiratory tract, both alone and simultaneously with other synergistic processes (coagulation, hygroscopic growth) that dynamically modify the particle size distribution. Model results compare favorably with clinical data available on CSP deposition in the lungs and indicate that a combination of cloud behavior, hygroscopic growth, and coagulation may explain the preferential proximal deposition of smoke particles in the tracheobronchial region.

HEAT TRANSFER PROPERTIES OF DRY AND WET FURS OF DAIRY COWS

ABSTRACT Heat and mass transfer properties of an Israeli-Holstein dairy cow fur were examined. The skin was stretched over a heat flux assembly, inserted into a wind tunnel, and heat and mass transfer were examined at different air velocities with fur either dry or wet. The dry coat was divided into two layers the fur and the boundary layer. The thermal resistance of the fur itself hardly changed with air velocity. The resistance to heat transfer of the coat boundary layer was found to be proportional to the square root of air velocity, similar to a flat plate. However, at low air velocity, the resistance of the coat boundary layer was found to be somewhat lower than that of a plate, while at high air velocities, it was higher than that of a plate. For the wet fur, the efficiency of forced evaporative cooling was determined by a single parameter of wettedness, which equaled unity for a saturated fur and decreased as the coat got drier. TTiis parameter is specific for a given coat and is directly related to the water content within the fur. Experiments were performed to measure the relationship between the wettedness and the amount of water sprinkled over the fur. The maximum water content of a coat wet by means of a commercially available sprinkler was some 230 g/m2 which corresponded to wettedness of 0.6. The results of this investigation may be used to design the most cost effective procedure of forced evaporative cooling for the relief of heat stress in cattle

Estimating daily PM2.5 and PM10 across the complex geo-climate region of Israel using MAIAC satellite-based AOD data

Estimates of exposure to PM2.5 are often derived from geographic characteristics based on land-use regression or from a limited number of fixed ground monitors. Remote sensing advances have integrated these approaches with satellite-based measures of aerosol optical depth (AOD), which is spatially and temporally resolved, allowing greater coverage for PM2.5 estimations. Israel is situated in a complex geo-climatic region with contrasting geographic and weather patterns, including both dark and bright surfaces within a relatively small area. Our goal was to examine the use of MODIS-based MAIAC data in Israel, and to explore the reliability of predicted PM2.5 and PM10 at a high spatiotemporal resolution. We applied a three stage process, including a daily calibration method based on a mixed effects model, to predict ground PM2.5 and PM10 over Israel. We later constructed daily predictions across Israel for 2003-2013 using spatial and temporal smoothing, to estimate AOD when satellite data were missing. Good model performance was achieved, with out-of-sample cross validation R2 values of 0.79 and 0.72 for PM10 and PM2.5, respectively. Model predictions had little bias, with cross-validated slopes (predicted vs. observed) of 0.99 for both the PM2.5 and PM10 models. To our knowledge, this is the first study that utilizes high resolution 1km MAIAC AOD retrievals for PM prediction while accounting for geo-climate complexities, such as experienced in Israel. This novel model allowed the reconstruction of long- and short-term spatially resolved exposure to PM2.5 and PM10 in Israel, which could be used in the future for epidemiological studies.

Improved retrieval of PM2.5 from satellite data products using non-linear methods

Satellite observations may improve the areal coverage of particulate matter (PM) air quality data that nowadays is based on surface measurements. Three statistical methods for retrieving daily PM2.5 concentrations from satellite products (MODIS-AOD, OMI-AAI) over the San Joaquin Valley (CA) are compared--Linear Regression (LR), Generalized Additive Models (GAM), and Multivariate Adaptive Regression Splines (MARS). Simple LRs show poor correlations in the western USA (R(2) ~/= 0.2). Both GAM and MARS were found to perform better than the simple LRs, with a slight advantage to the MARS over the GAM (R(2) = 0.71 and R(2) = 0.61, respectively). Since MARS is also characterized by a better computational efficiency than GAM, it can be used for improving PM2.5 retrievals from satellite aerosol products. Reliable PM2.5 retrievals can fill in missing surface measurements in areas with sparse ground monitoring coverage and be used for evaluating air quality models and as exposure metrics in epidemiological studies.

Spatial analysis of air pollution and cancer incidence rates in Haifa Bay, Israel

The Israel National Cancer Registry reported in 2001 that cancer incidence rates in the Haifa area are roughly 20% above the national average. Since Haifa has been the major industrial center in Israel since 1930, concern has been raised that the elevated cancer rates may be associated with historically high air pollution levels. This work tests whether persistent spatial patterns of metrics of chronic exposure to air pollutants are associated with the observed patterns of cancer incidence rates. Risk metrics of chronic exposure to PM(10), emitted both by industry and traffic, and to SO(2), a marker of industrial emissions, was developed. Ward-based maps of standardized incidence rates of three prevalent cancers: Non-Hodgkins lymphoma, lung cancer and bladder cancer were also produced. Global clustering tests were employed to filter out those cancers that show sufficiently random spatial distribution to have a nil probability of being related to the spatial non-random risk maps. A Bayesian method was employed to assess possible associations between the morbidity and risk patterns, accounting for the ward-based socioeconomic status ranking. Lung cancer in males and bladder cancer in both genders showed non-random spatial patterns. No significant associations between the SO(2)-based risk maps and any of the cancers were found. Lung cancer in males was found to be associated with PM(10), with the relative risk associated with an increase of 1 microg/m(3) of PM(10) being 12%. Special consideration of wards with expected rates <1 improved the results by decreasing the variance of the spatially correlated residual log-relative risk.