Table of contents

Prior studies suggest ubiquitous fetal exposure to the endocrine disrupting chemical bisphenol A (BPA). Insufficient information is available on the effects of BPA in underserved urban populations in the US. We describe prenatal BPA exposures in a predominately Afro-Caribbean immigrant population. Maternal third-trimester urinary concentrations of total BPA were measured in 181 mothers in Brooklyn, NY from 2007 to 2009. Mothers aged 18–45 y presenting at a prenatal clinic consented to study participation. Spot urine samples were collected once between the sixth and ninth month of pregnancy. The geometric mean concentration of total BPA was 0.12 μg l⁻¹ (95% CI: 0.05–0.31). Total BPA concentrations were above the limit of detection in 9% of the mothers. Our results suggest that prenatal BPA exposure is low to non-detectable (< 0.02 μg l⁻¹ in urine) among African American and Afro-Caribbean immigrant women residing in Brooklyn, NY. These results contradict evidence of prenatal exposure in prior studies of urban populations. Further studies should be conducted to determine whether there are associations between recent immigrant status and BPA exposures during pregnancy.

Urban development can have negative impacts on the environment through various mechanisms. While many air quality studies have been carried out in more developed nations, Eastern Caribbean (EC) countries remain understudied. This study aims to estimate the concentrations of air pollutants in the EC nation of St. Kitts and Nevis. Transport, recreation and construction sites were selected randomly using local land use records. Pollutant levels were measured repeatedly for numerous 1-hour intervals in each location between October 2015 and November 2018. Weather trends and land use characteristics were collected concurrent to sampling. Across 27 sites, mean NO₂, O₃, SO₂, PM₁₀ and PM_2.5 levels were 26.61 ppb (range: 0–306 ppb), 11.94 ppb (0–230 ppb), 27.9 ppb (0–700 ppb), 52.9 μg m⁻³ (0–10,400 μg m⁻³) and 29.8 μg m⁻³ (0–1556 μg m⁻³), respectively. Pollutants were elevated in high urban areas and generally significantly positively correlated with each other, with the exception of PM₁₀. NO₂ levels in construction areas were generally comparable to those in transportation areas and higher than in recreation areas. O₃ levels were lower in construction than recreation and transport areas. SO₂ concentrations were lower in construction and recreation compared to transport sites. Construction and recreation PM₁₀ levels exceeded transport sites, while PM_2.5 was highest in construction areas. Additional bivariate and multivariate analysis were conducted to assess whether various meteorological, temporal and land use factors including rain, tour season and urban features explained variability in air pollutant concentrations. Tourist season and specific months, more than any other factors, contributed most to variability in pollutant concentrations. These new measurements of air pollution concentrations in an understudied nation may have important implications for health outcomes among exposed EC residents, and provide critical data for future exposure and epidemiologic research and environmental policy.

Fine particles (PM2.5) can penetrate buildings through ventilation and air conditioning systems, exposing indoors workers to pollution levels similar to those prevailing outdoors. This letter investigates the immediate influence of fine particle pollution on the productive activity of local government bureaucracies, linking novel data on the daily output of local governments in municipalities of the Athens metropolitan area, Greece, to PM2.5 levels recorded nearby. To address biases introduced by omitted variables and measurement error, I use the plausibly exogenous variation introduced by the basin's horizontal ventilation, instrumenting PM2.5 levels with local wind strength. Estimates suggest a statistically and quantitatively significant negative effect from PM2.5 on the output of public administrations. Increasing PM2.5 levels by 1% decreases the activity proxy by around 0.25%. Results point to the influence PM2.5 can have on activities that are mentally but not physically demanding and suggest that costs from PM2.5 will increase with the share of global income produced by office workers.

Climate change is challenging the sustained delivery of ecosystem services from urban agriculture. Extreme, prolonged drought in combination with high heat events affect urban crop production due to limited water availability and affect environmental management and adaptation to environmental conditions. In this study, we use urban community gardens in central coast California as a system to investigate how people are adapting their management behaviors over three time periods—before, during and after the longest drought in California's recent history. We specifically ask how behavioral change is impacted by water policies and gardener characteristics (including gardening experience, formal education, drought concern, and relationship to nature). Through structural equation modeling and multivariate analyses, we show that nature relatedness and gardening experience impact drought concern which in turn impact behavioral change, and potentially gardener's ability to sustainably manage water and to adapt to drought conditions. Planting motivations are also important, influencing people's adoption and retention of practices over time. Yet where concern may be absent, water policies are able to promote and maintain behavioral change and conservation-based practice adoption. Thus, environmental awareness and experience in combination with policies are needed to promote and support proactive behavioral change and adaptation to create resilient urban food production systems under climate change.

In 2018 Sterman et al (2018a) published a simple dynamic lifecycle analysis (DLCA) model for forest-sourced bioenergy. The model has been widely cited since its publication, including widespread reporting of the model's headline results within the media. In adapting a successful replication of the Sterman et al (2018a) model with open-source software, we identified a number of changes to input parameters which improved the fit of the model's forest site growth function with its training data. These relatively small changes to the input parameters result in relatively large changes to the model predictions of forest site carbon uptake: up to 92 tC.ha⁻¹ or 18% of total site carbon at year 500. This change in estimated site carbon resulted in calculated payback periods (carbon sequestration parity) which differed by up to 54 years in a clear-fell scenario when compared with results obtained using previously published parameters. Notably, this uncertainty was confined to forests which were slower growing and where the model's training dataset was not sufficiently long for forests to reach maturity. We provide improved parameterisations for all forest types used within the original Sterman et al (2018a) paper, and propose that these provide better fits to the underlying data. We also provide margins of error for the generated growth curves to indicate the wide range of possible results possible with the model for some forest types. We conclude that, while the revised model is able to reproduce the earlier Sterman et al (2018a) results, the headline figures from that paper depend heavily on how the forest growth curve is fitted to the training data. The resulting uncertainty in payback periods could be reduced by either obtaining more extensive training data (including mature forests of all types) or by modification of the forest growth function.

Three months comparison of hourly solar radiation forecasting from 1st January to 31st March 2017 between Weather Research and Forecasting (WRF) mesoscale model and Long short-term memory (LSTM) algorithm is presented in this study. One-way grid nesting technique of the WRF model is applied for the simulation with a six-hourly input dataset downloaded from the National Oceanic and Atmospheric Administration - National Operational Model Archive and Distribution System (NOMADS) website. Three years'data of solar radiation from 1st January 2014 to 31st December 2016 are used as input data for Long Short Term Memory (LSTM) algorithm to simulate solar radiation. The results show the root mean square error of the LSTM algorithm is 310 W m⁻² higher compared to 210 W m⁻² from the WRF model. The MBE and the nMBE of the WRF model are obtained positive value 96 W m⁻² and 9% compared to −101 W m⁻² and −9% of LSTM for 2160 h prediction. Meanwhile, the performance error percentage of WRF is 19% lower compared to 28% of LSTM for the nRMSE error metric. Although this study found that the WRF model performed better and lower error compared to the LSTM algorithm, however, it also recommends the LSTM algorithm configuration can be used for long-term prediction.

Understanding the underlying values, beliefs and emotions that influence the public's perceptions and opinions on climate change (CC) is increasingly important, as CC is a complex and politicized phenomenon. Additionally, optimizing messaging for communicating CC and encouraging greater mitigation behavior can yield significant benefits to global stakeholders. Here we evaluated the effectiveness of informative, persuasive and empathic message frames about a major climate-related local flooding event through online surveys administered to 370 adults in Manitoba and Saskatchewan, Canada and 360 adults in Queensland, Australia. Measures of trust in climate communicators and climate science, concern over CC effects, and belief that most recent floods are due to CC were assessed before and after message exposure, along with related values, beliefs and emotions. Willingness to support pro-environmental groups was assessed as a proxy measure of behavioural intent. Cumulative odds ordinal regression and multinomial regression were used to predict group membership (no support, passive support, active support). Political affiliation, trust and belief in CC, belief in anthropogenic CC, pro-environmental values, and, in some regressions, previous flood exposure, were significant predictors of activism support. Respondents who received the empathic message frame and had low to medium pro-environmental values were more likely to believe the link between flooding and CC compared to those who received the informative message frame. These results, including the finding that some elicited emotions predicted behavioral intent, provide insight into how to construct climate information for groups with varying beliefs, values and experiences, to reduce climate skepticism and encourage pro-environmental behavior.