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The ventilation rate (VR) is a key parameter affecting indoor environmental quality (IEQ) and the energy consumption of buildings. This paper reviews the use of CO2 as a “natural” tracer gas for estimating VRs, focusing on applications in school classrooms. It provides details and guidance for the steady-state, build-up, decay and transient mass balance methods. An extension to the build-up method and an analysis of the post-exercise recovery period that can increase CO2 generation rates are presented. Measurements in four mechanically-ventilated school buildings demonstrate the methods and highlight issues affecting their applicability. VRs during the school day fell below recommended minimum levels, and VRs during evening and early morning were on the order of 0.1 h−1, reflecting shutdown of the ventilation systems. The transient mass balance method was the most flexible and advantageous method given the low air change rates and dynamic occupancy patterns observed in the classrooms. While the extension to the build-up method improved stability and consistency, the accuracy of this and the steady-state method may be limited. Decay-based methods did not reflect the VR during the school day due to heating, ventilation and air conditioning (HVAC) system shutdown. Since the number of occupants in classrooms changes over the day, the VR expressed on a per person basis (e.g., L·s−1·person−1) depends on the occupancy metric. If occupancy measurements can be obtained, then the transient mass balance method likely will provide the most consistent and accurate results among the CO2-based methods. Improved VR measurements can benefit many applications, including research examining the linkage between ventilation and health.

An interim report of a case-control study was conducted to explore the role of environmental factors in the development of amyotrophic lateral sclerosis (ALS). Sixty-six cases and 66 age- and gender-matched controls were recruited. Detailed information regarding residence history, occupational history, smoking, physical activity, and other factors was obtained using questionnaires. The association of ALS with potential risk factors, including smoking, physical activity and chemical exposure, was investigated using conditional logistic regression models. As compared to controls, a greater number of our randomly selected ALS patients reported exposure to fertilizers to treat private yards and gardens and occupational exposure to pesticides in the last 30 years than our randomly selected control cases. Smoking, occupational exposures to metals, dust/fibers/fumes/gas and radiation, and physical activity were not associated with ALS when comparing the randomly selected ALS patients to the control subjects. To further explore and confirm results, exposures over several time frames, including 0-10 and 10-30 years earlier, were considered, and analyses were stratified by age and gender. Pesticide and fertilizer exposure were both significantly associated with ALS in the randomly selected ALS patients. While study results need to be interpreted cautiously given the small sample size and the lack of direct exposure measures, these results suggest that environmental and particularly residential exposure factors warrant close attention in studies examining risk factors of ALS.

Both the recent classification of naphthalene as a possible human carcinogen and its ubiquitous presence motivate this critical review of naphthalene’s sources and exposures. We evaluate the environmental literature on naphthalene published since 1990, drawing on nearly 150 studies that report emissions and concentrations in indoor, outdoor and personal air. While naphthalene is both a volatile organic compound and a polycyclic aromatic hydrocarbon, concentrations and exposures are poorly characterized relative to many other pollutants. Most airborne emissions result from combustion, and key sources include industry, open burning, tailpipe emissions, and cigarettes. The second largest source is off-gassing, specifically from naphthalene’s use as a deodorizer, repellent and fumigant. In the U.S., naphthalene’s use as a moth repellant has been reduced in favor of para-dichlorobenzene, but extensive use continues in mothballs, which appears responsible for some of the highest indoor exposures, along with off-label uses. Among the studies judged to be representative, average concentrations ranged from 0.18 to 1.7 μg m-3 in non-smoker’s homes, and from 0.02 to 0.31 μg m-3 outdoors in urban areas. Personal exposures have been reported in only three European studies. Indoor sources are the major contributor to (non-occupational) exposure. While its central tendencies fall well below guideline levels relevant to acute health impacts, several studies have reported maximum concentrations exceeding 100 μg m-3, far above guideline levels. Using current but draft estimates of cancer risks, naphthalene is a major environmental risk driver, with typical individual risk levels in the 10-4 range, which is high and notable given that millions of individuals are exposed. Several factors influence indoor and outdoor concentrations, but the literature is inconsistent on their effects. Further investigation is needed to better characterize naphthalene’s sources and exposures, especially for indoor and personal measurements.

BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal, progressive neurogenerative disease caused by combined genetic susceptibilities and environmental exposures. Identifying and validating these exposures are of paramount importance to modify disease risk. We previously reported that persistent organic pollutants (POPs) associate with ALS risk and survival and aimed to replicate these findings in a new cohort.MethodParticipants with and without ALS recruited in Michigan provided plasma samples for POPs analysis by isotope dilution with triple quadrupole mass spectrometry. ORs for risk models and hazard ratios for survival models were calculated for individual POPs. POP mixtures were represented by environmental risk scores (ERS), a summation of total exposures, to evaluate the association with risk (ERSrisk) and survival (ERSsurvival).ResultsSamples from 164 ALS and 105 control participants were analysed. Several individual POPs significantly associated with ALS, including 8 of 22 polychlorinated biphenyls and 7 of 10 organochlorine pesticides (OCPs). ALS risk was most strongly represented by the mixture effects of OCPs alpha-hexachlorocyclohexane, hexachlorobenzene, trans-nonachlor and cis-nonachlor and an interquartile increase in ERSrisk enhanced ALS risk 2.58 times (p<0.001). ALS survival was represented by the combined mixture of all POPs and an interquartile increase in ERSsurvival enhanced ALS mortality rate 1.65 times (p=0.008).ConclusionsThese data continue to support POPs as important factors for ALS risk and progression and replicate findings in a new cohort. The assessments of POPs in non-Michigan ALS cohorts are encouraged to better understand the global effect and the need for targeted disease risk reduction strategies.

BackgroundEnvironmental exposures contribute to the pathogenesis of amyotrophic lateral sclerosis (ALS), a fatal and progressive neurological disease. Identification of these exposures is important for targeted screening and risk factor modification.ObjectiveTo identify occupational exposures that are associated with a higher risk of ALS using both survey and standard occupational classification (SOC) coding procedures, and to highlight how exposure surveys can complement SOC coding.MethodsALS participants and neurologically healthy controls recruited in Michigan completed a detailed exposure assessment on their four most recent and longest held occupations. Exposure scores were generated from the exposure survey, and occupations were assigned to SOC codes by experienced exposure scientists.ResultsThis study included 381 ALS and 272 control participants. ALS participants reported higher duration-adjusted occupational exposure to particulate matter (OR = 1.45, 95% CI 1.19–1.78, p < 0.001), volatile organic compounds (OR = 1.22, 95% CI 1.02–1.45, p = 0.029), metals (OR = 1.48, 95% CI 1.21–1.82, p < 0.001), and combustion and diesel exhaust pollutants (OR = 1.20, 95% CI 1.01–1.43, p = 0.041) prior to ALS diagnosis, when adjusted for sex, age, and military service compared to controls. In multivariable models, only occupational exposure to metals remained significant risk (OR = 1.56, 95% CI 1.11–2.20, p = 0.011), although in an adaptive elastic net model, particulate matter (OR = 1.203), pesticides (OR = 1.015), and metals (1.334) were all selected as risk factors. Work in SOC code “Production Occupations” was associated with a higher ALS risk. SOC codes “Building and Grounds Cleaning and Maintenance Occupations”, “Construction and Extraction Occupations”, “Installation, Maintenance, and Repair Occupations”, and “Production Occupations” were all associated with a higher exposure to metals as determined using survey data.DiscussionOccupational exposure to particulate matter, volatile organic compounds, metals, pesticides, and combustion and diesel exhaust and employment in “Production Occupations” was associated with an increased ALS risk in this cohort.

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM2.5), ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM2.5 and O3 exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO2 exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO2 exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disease with a median survival of only 2 to 4 years from diagnosis. Improved tools are needed to shorten diagnostic delays and improve prognostication to benefit clinical care. Herein, we profiled whole blood gene expression by RNA sequencing in a large cohort of ALS participants ( n  = 422) versus controls ( n  = 272). Several machine learning classifiers trained on our detailed gene expression dataset accurately predicted case-control status, including in a fully independent external test cohort, achieving an area under the receiver operating characteristic curve of 0.894 with the best performing model. Integrating gene expression features with clinical variables improved our ability to discriminate ALS cases into shorter, intermediate, and longer survival in an external dataset. Finally, we identified ALS-relevant pathways in our blood transcriptomics dataset as well as “core genes” that overlapped with gene expression changes occurring in the primary disease tissue, facilitating a drug perturbation analysis that identified several candidates. Overall, our results highlight the potential diagnostic and prognostic applications of whole blood gene expression data, with important implications for improving ALS clinical care. Zhao et al. generated models from whole blood gene expression to predict amyotrophic lateral sclerosis case-control status and combined gene features with clinical variables to predict survival, both validated in an external independent dataset.

Municipal solid waste landfills are major contributors to anthropogenic emissions of methane (CH4), which is the major component of natural gas, a potent greenhouse gas, and a precursor for the formation of tropospheric ozone. The development of sensitive, selective, and fast-response instrumentation allows the deployment of mobile measurement platforms for CH4 measurements at landfills. The objectives of this study are to use mobile monitoring to measure ambient levels of CH4 at eight large operating landfills in southeast Michigan, USA; to characterize diurnal, daily and spatial variation in CH4 levels; and to demonstrate the influence of meteorological factors. Elevated CH4 levels were typically found along the downwind side or corner of the landfill. Levels peaked in the morning, reaching 38 ppm, and dropped to near-baseline levels during midday. Repeat visits showed that concentrations were highly variable. Some variation was attributable to the landfill size, but both mechanistically-based dilution-type models and multivariate models identified that wind speed, boundary layer height, barometric pressure changes, and landfill temperature were key determinants of CH4 levels. Collectively, these four factors explained most (r2 = 0.89) of the variation in the maximum CH4 levels at the landfill visited most frequently. The study demonstrates the ability to assess spatial and temporal variation in CH4 levels at landfills using mobile monitoring along perimeter roads. Such monitoring can identify the location of leaks and the best locations for long-term emission monitoring using fixed site monitors.

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.

BackgroundThe pathogenesis of amyotrophic lateral sclerosis (ALS) involves both genetic and environmental factors. This study investigates associations between metal measures in plasma and urine, ALS risk and survival and exposure sources.MethodsParticipants with and without ALS from Michigan provided plasma and urine samples for metal measurement via inductively coupled plasma mass spectrometry. ORs and HRs for each metal were computed using risk and survival models. Environmental risk scores (ERS) were created to evaluate the association between exposure mixtures and ALS risk and survival and exposure source. ALS (ALS-PGS) and metal (metal-PGS) polygenic risk scores were constructed from an independent genome-wide association study and relevant literature-selected single-nucleotide polymorphisms.ResultsPlasma and urine samples from 454 ALS and 294 control participants were analysed. Elevated levels of individual metals, including copper, selenium and zinc, significantly associated with ALS risk and survival. ERS representing metal mixtures strongly associated with ALS risk (plasma, OR=2.95, CI=2.38–3.62, p<0.001; urine, OR=3.10, CI=2.43–3.97, p<0.001) and poorer ALS survival (plasma, HR=1.37, CI=1.20–1.58, p<0.001; urine, HR=1.44, CI=1.23–1.67, p<0.001). Addition of the ALS-PGS or metal-PGS did not alter the significance of metals with ALS risk and survival. Occupations with high potential of metal exposure associated with elevated ERS. Additionally, occupational and non-occupational metal exposures were associated with measured plasma and urine metals.ConclusionMetals in plasma and urine associated with increased ALS risk and reduced survival, independent of genetic risk, and correlated with occupational and non-occupational metal exposures. These data underscore the significance of metal exposure in ALS risk and progression.