Search

Despite reported health benefits of urban greenspace (gs), the epidemiological evidence is less clear for allergic disease. To address a limitation of previous research, we examined the associations of medium- and high-resolution residential gs measures and tree and/or grass canopies with allergic outcomes for children enrolled in the longitudinal cincinnati childhood allergy and air pollution study (ccaaps). We estimated residential gs based on 400 m radial buffers around participant addresses (n = 478) using the normalized differential vegetation index (ndvi) and land cover-derived urban greenspace (ugs) (tree and grass coverage, combined and separate) at 30 m and 1.5–2.5 m resolution, respectively. Associations between outdoor aeroallergen sensitization and allergic rhinitis at age 7 and residential gs measures at different exposure windows were examined using multivariable logistic regression models. A 10% increase in ugs-derived grass coverage was associated with an increased risk of sensitization to grass pollens (adjusted odds ratio [aor]: 1.27; 95% confidence interval = 1.02–1.58). For each 10% increase in ugs-derived tree canopy coverage, nonstatistically significant decreased odds were found for grass pollen sensitization, tree pollen sensitization, and sensitization to either (aor range = 0.87–0.94). Results similar in magnitude to ugs-tree canopy coverage were detected for ndvi and allergic sensitizations. High-resolution (down to 1.5 m) gs measures of grass- and tree-covered areas showed associations in opposite directions for different allergy outcomes. These data suggest that measures strongly correlated with tree canopy (e.g., ndvi) may be insufficient to detect health effects associated with proximity to different types of vegetation or help elucidate mechanisms related to specific gs exposure pathways.

Studying the impacts of prenatal atmospheric heavy-metal exposure is challenging, because biological exposure monitoring does not distinguish between specific sources, and high-resolution air monitoring data is lacking for heavy metals. Bioindicators - animal or plant species that can capture environmental quality - are a low-cost tool for evaluating exposure to atmospheric heavy-metal pollution that have received little attention in the public-health literature. We obtained birth records for Portland, Oregon live births (2008-2014) and modeled metal concentrations derived from 346 samples of moss bioindicators collected in 2013. Exposure estimates were assigned using mother's residential address at birth for six metals with known toxic and estrogenic effects (arsenic, cadmium, chromium, cobalt, nickel, lead). Associations were evaluated for continuous (cts) and quartile-based (Q) metal estimates and three birth outcomes (preterm birth (PTB;37 weeks)), very PTB (vPTB;32 weeks), small for gestational age (SGA; 10th percentile of weight by age and sex)) using logistic regression models with adjustment for demographic characteristics, and stratified by maternal race. Chromium and cobalt were associated with increased odds of vPTB (chromium - odds ratio (OR)

Polycyclic aromatic hydrocarbons (PAHs) are widespread pollutants that are costly to measure with air quality instruments. We used a spatially balanced sample of an epiphytic moss, Orthotrichum lyellii, to develop fine-scale maps of atmospheric PAHs across residential areas of Portland, Oregon. The unusual abundance of this stress-tolerant species enabled us to systematically collect a large sample (n = 350) on a 1 × 1km grid across the city. This provided the unique opportunity to investigate potential effects of more than 20 factors on PAH concentrations in moss, including emissions sources, tree canopy cover, relative elevation, short-term weather, and nuance of sample collection. We measured 20 PAHs in moss samples and assigned them to 4 groups for analysis based on molecular weight (MW): the Naphthalene, Low, Medium, and High MW groups. Spatial regression models explained a modest amount of variation in PAH concentrations in moss, with spatial structure increasing as MW increased. Motor vehicle emissions were the only PAH source we detected and was highly significant in all models. Deciduous tree cover correlated with lower PAH concentrations in moss for all except the high MW group. Short-term weather and nuance of sample collection, like height on the sampled tree, tree taxonomic family, and whether sample was primarily collected from a branch or bole, had surprisingly large effects on all groups. Overall, results aligned with expectations based on each group's dispersal potential due to MW. All PAH groups were highest over downtown Portland, along major highways, and lowest in parks and outer neighborhoods, although the details varied. Our results supported two common themes in studies directly measuring PAHs: the importance of roads as an emissions source in residential areas, and the potential for trees to reduce atmospheric concentrations. Moreover, the large effects of weather and sampling nuance have critical implications for the design of future studies.

Background:The biodiversity hypothesis posits that declining biodiversity may be responsible, at least in part, for the global increase in immune diseases. However, few studies have been able to demonstrate a link between exposure to biodiversity and specific health outcomes. Methods: We test whether exposure to plant diversity protects against childhood acute lymphoblastic leukemia (ALL) by promoting immune maturation. Our sample consisted of all children born in New Zealand from 1998-2011 (n=899,126; 264 ALL cases), which we followed from birth to age five. We calculated plant-diversity metrics using the Global Biodiversity Information Facility, which contains over two million geocoded plant records in New Zealand. Findings: Consistent with previous research, children who had always lived in an urban area, or who had an older mother, were at greater risk for ALL, whereas children with older siblings were at lower risk. In addition, we found that plant-diversity metrics based on the maximum number of plant genera a child was exposed to during the first two years of life were protective of ALL. Specifically, exposure to the highest tertile of plant diversity was associated with a reduction in ALL risk of 35% (95% CI: 11%-53%). Interpretation: Exposure to plant diversity, and associated microbial communities, may be a viable public-health intervention to reduce the risk of ALL and possibly other immune diseases. Funding Statement: Geoffrey Donovan acknowledges the receipt of a fellowship from the OECD Co-operative Research Programme: Biological Resource Management for Sustainable Agricultural Systems in 2019. No other funding was received. Declaration of Interests: The authors declare no competing financial interests. Ethics Approval Statement: Our study was classified as out of scope by the New Zealand Health and Disabilities Ethics Committee, approved by Statistics New Zealand (approval # MAA2019-07), and conducted in accordance with national and international ethical guidelines for observational studies.

We assessed the association between the natural environment and asthma in 49,956 New Zealand children born in 1998 and followed up until 2016 using routinely collected data. Children who lived in greener areas, as measured by the normalized difference vegetation index, were less likely to be asthmatic: a 1 s.d. increase in normalized difference vegetation index was associated with a 6.0% (95% CI 1.9-9.9%) lower risk of asthma. Vegetation diversity was also protective: a 1 s.d. increase in the number of natural land-cover types in a child's residential meshblock was associated with a 6.7% (95% CI 1.5-11.5%) lower risk. However, not all land-cover types were protective. A 1 s.d. increase in the area covered by gorse (Ulex europaeus) or exotic conifers, both non-native, low-biodiversity land-cover types, was associated with a 3.2% (95% CI 0.0-6.0%) and 4.2% (95% CI 0.9-7.5%) increased risk of asthma, respectively. The results suggest that exposure to greenness and vegetation diversity may be protective of asthma.

Background: Several recent studies have identified a relationship between the natural environment and improved health outcomes. However, for practical reasons, most have been observational, cross-sectional studies. Purpose: A natural experiment, which provides stronger evidence of causality, was used to test whether a major change to the natural environment—the loss of 100 million trees to the emerald ash borer, an invasive forest pest— has influenced mortality related to cardiovascular and lower- respiratory diseases. Methods: Two fixed-effects regression models were used to estimate the relationship between emerald ash borer presence and county-level mortality from 1990 to 2007 in 15 U.S. states, while controlling for a wide range of demographic covariates. Data were collected from 1990 to 2007, and the analyses were conducted in 2011 and 2012. Results: There was an increase in mortality related to cardiovascular and lower-respiratory-tract illness in counties infested with the emerald ash borer. The magnitude of this effect was greater as infestation progressed and in counties with above-average median household income. Across the 15 states in the study area, the borer was associated with an additional 6113 deaths related to illness of the lower respiratory system, and 15,080 cardiovascular-related deaths. Conclusions: Results suggest that loss of trees to the emerald ash borer increased mortality related to cardiovascular and lower-respiratory-tract illness. This finding adds to the growing evidence that

Urban networks of air-quality monitors are often too widely spaced to identify sources of air pollutants, especially if they do not disperse far from emission sources. The objectives of this study were to test the use of moss bio-indicators to develop a fine-scale map of atmospherically-derived cadmium and to identify the sources of cadmium in a complex urban setting. We collected 346 samples of the moss Orthotrichum lyellii from deciduous trees in December, 2013 using a modified randomized grid-based sampling strategy across Portland, Oregon. We estimated a spatial linear model of moss cadmium levels and predicted cadmium on a 50 m grid across the city. Cadmium levels in moss were positively correlated with proximity to two stained-glass manufacturers, proximity to the Oregon–Washington border, and percent industrial land in a 500 m buffer, and negatively correlated with percent residential land in a 500 m buffer. The maps showed very high concentrations of cadmium around the two stained-glass manufacturers, neither of which were known to environmental regulators as cadmium emitters. In addition, in response to our findings, the Oregon Department of Environmental Quality placed an instrumental monitor 120 m from the larger stained-glass manufacturer in October, 2015. The monthly average atmospheric cadmium concentration was 29.4 ng/m3, which is 49 times higher than Oregon's benchmark of 0.6 ng/m3, and high enough to pose a health risk from even short-term exposure. Both stained-glass manufacturers voluntarily stopped using cadmium after the monitoring results were made public, and the monthly average cadmium levels precipitously dropped to 1.1 ng/m3 for stained-glass manufacturer #1 and 0.67 ng/m3 for stained-glass manufacturer #2.

Mosses accumulate pollutants from the atmosphere and can serve as an inexpensive screening tool for mapping air quality and guiding the placement of monitoring instruments. We measured 22 elements using 346 moss samples collected across Portland, Oregon, in December 2013. Our objectives were to develop citywide maps showing concentrations of each element in moss and identify potential air pollution “hotspots.” We used simple dot maps, histograms, and summary statistics to describe the distribution of each element. Fifteen metals had highly right-skewed distributions, indicating high metal concentrations (relative to concentrations mea-sured in our dataset) in moss at one or more locations. These metals included high-priority toxics such as cadmium, nickel, lead, and arsenic. Past research shows that element concentrations in moss reflect atmospheric concentrations, although the strength of these relationships varies by element and is unknown for the elements we sampled. Therefore, atmospheric concentrations would need to be measured by an air quality monitor in order to determine whether hotspots suggested by the moss indicator are problematic or pose a health risk. We provide the raw data for all elements we measured to enable scientists, regulators, and citizens to further investigate the importance and possible sources of moss-identified hotspots.

Data from the Women's Health Initiative were used to quantify the relationship between the loss of trees to an invasive forest pest-the emerald ash borer-and cardiovascular disease. We estimated a semi-parametric Cox proportional hazards model of time to cardiovascular disease, adjusting for confounders. We defined the incidence of cardiovascular disease as acute myocardial infarction requiring overnight hospitalization, silent MI determined from serial electrocardiograms, ischemic or hemorrhagic stroke, or death from coronary heart disease. Women living in a county infested with emerald ash borer had an increased risk of cardiovascular disease (HR=1.25, 95% CI: 1.20-1.31).

We investigated the potential of using unclassified spectral data for predicting the distribution of three bird species over a ¨400,000 ha region of Michigan’s Upper Peninsula using Landsat ETM+ imagery and 433 locations sampled for birds through point count surveys. These species, Black-throated Green Warbler, Nashville Warbler, and Ovenbird, were known to be associated with forest understory features during breeding. We examined the influences of varying two spatially explicit classification parameters on prediction accuracy: 1) the window size used to average spectral values in signature creation and 2) the threshold distance required for bird detections to be counted as present. Two accuracy measurements, proportion correctly classified (PCC) and Kappa, of maps predicting species’ occurrences were calculated with ground data not used during classification. Maps were validated for all three species with Kappa values >0.3 and PCC >0.6. However, PCC provided little information other than a summary of sample plot frequencies used to classify species’ presence and absence. Comparisons with rule-based maps created using the approach of Gap Analysis showed that spectral information predicted the occurrence of these species that use forest subcanopy components better than could be done using known land cover associations (Kappa values 0.1 to 0.3 higher than Gap Analysis maps). Accuracy statistics for each species were affected in different ways by the detection distance of point count surveys used to stratify plots into presence and absence classes. Moderate-to-large detection distances (100 m and 180 m) best classified maps of Blackthroated Green Warbler and Nashville Warbler occurrences, while moderate detection distances (50 m and 100 m), which ignored remote observations, provided the best source of information for classification of Ovenbird occurrence. Window sizes used in signature creation also influenced accuracy statistics but to a lesser extent. Highest Kappa values of majority maps were typically obtained using moderate window sizes of 9 to 13 pixels (0.8 to 1.2 ha), which are representative of the study species territory sizes. The accuracy of wildlife occurrence maps classified from spectral data will therefore differ given the species of interest, the spatial precision of occurrence records used as ground references and the number of pixels included in spectral signatures. For these reasons, a quantitative examination is warranted to determine how subjective decisions made during image classifications affect prediction accuracies.