Journal of Exposure Science and Environmental Epidemiology最新文献

Background: Air pollution is a known risk factor for non-communicable diseases that causes substantial premature death globally. Rapid urban growth, burning of biomass and solid waste, unpaved sections of the road network, rising numbers of vehicles, some with highly polluting engines, contribute to the poor air quality in Kampala.

Objective: To provide evidence-based estimates of air pollution attributable mortality in Kampala city, with focus on ambient fine particulate matter (PM_2.5).

Methods: We utilized a time series design and prospectively collected data on daily ambient PM_2.5 concentration levels in micrograms per cubic meter (μg/m³) using a Beta Attenuation Monitor (BAM-1022) in Kampala city, Uganda. We combined the PM_2.5 data with all-cause mortality data obtained from the Uganda Bureau of Statistics and the Ministry of Health in Kampala. We calculated attributable risk estimates for mortality using the WHO AirQ+ tools.

Results: Overall, the annual average concentration for PM_2.5 for the period of 4 years, 2018-2021, was 39 μg/m³. There was seasonal variation, with the rainy season months (March-June and October-December) having lower values. PM_2.5 concentrations tend to be highest in the morning (09.00 h) and in the evening (21.00 h.) likely due to increased vehicular emissions as well as the influence of weather patterns (atmospheric temperature, relative humidity and wind). Saturday has the most pollution (daily average over 4 years of 41.2 μg/m³). Regarding attributable risk, we found that of all the deaths in Kampala, 2777 (19.3%), 2136 (17.9%), 1281 (17.9%) and 1063 (19.8%) were attributable to long-term exposure to air pollution (i.e., exposure to PM_2.5 concentrations above the WHO annual guideline of 5 μg/m³) from 2018 to 2021, respectively. For the 4 years and considering the WHO annual guideline as the reference, there were 7257 air pollution-related deaths in Kampala city.

Impact: Our study is the first to estimate air pollution attributable deaths in Kampala city considering the target as the WHO annual guideline value for PM_2.5 of 5 μg/m³. Our monitoring data show that fine particulate matter air pollution in Kampala is above the WHO Air Quality Guideline value, likely resulting in substantial adverse health effects and premature death. While further monitoring is necessary, there is a clear need for control measures to improve air quality in Kampala city.

Background: Environmental movements of the late 20th century resulted in sweeping legislation and regulatory actions to reduce the prevalence of diverse pollutants. Although the consequences of noise pollution to public health, environment, and the economy have been recognized over the same time period, noise has received far less policy attention. Correspondingly, even while evidence of the diverse and detrimental effects of noise pollution on human health has grown, solutions and actual reductions in environmental noise remain seemingly out of reach.

Objective: To address this shortcoming, we developed a prospectus for environmental noise reduction through technology-forcing policies. Technology-forcing describes intent to encourage technological solutions for pollution control through policy and regulations, and has been a critical component of national and global progress in reducing environmental pollutants.

Methods: We take advantage of the unique policy history for noise in the United States - which initially enacted, but then abandoned federal noise regulation. We compare this history against outcomes from contemporaneous environmental legislation for air, water, and occupational pollution control, to demonstrate the potential for technology-forcing to reduce noise pollution. Our review then identifies promising solutions, in the form of existing technologies suitable for innovation and diffusion through technology-forcing regulations and incentives.

Results: Based on this review, we outline a program for noise policy development to support efforts to reduce environmental noise pollution worldwide. The proposed program consists of three steps, which are to (i) identify dominant sources of noise pollution, (ii) combine legislative or regulatory provisions with suitable systems of enforcement and incentives, and (iii) anticipate and prepare for stages of technological change.

Impact statement: Analysis of noise policy often focuses on justifying the need to reduce noise pollution. In this article, we demonstrate how technology-forcing regulations could also promote much-needed innovation and diffusion of technologies to reduce environmental noise pollution. We first establish the potential for technology-forcing by comparing technology outcomes from environmental legislation passed contemporaneously to the inactive US Noise Control Act. We next review promising innovations available for diffusion in multiple sectors to reduce environmental noise. Lastly, we recommend a program to support development of technology-forcing noise policies, to help ensure that the benefits of reduced noise pollution are distributed equitably.

Background: Climate factors such as solar radiation could contribute to mood disorders, but evidence of associations between exposure to solar radiation and mood disorders is mixed and varies by region.

Objective: To evaluate the association of solar radiation with depression and distress among residents living in U.S. Gulf states.

Methods: We enrolled home-visit participants in the Gulf Long-Term Follow-up Study who completed validated screening questionnaires for depression (Patient Health Questionnaire-9, N = 10,217) and distress (Kessler Psychological Distress Questionnaire, N = 8,765) for the previous 2 weeks. Solar radiation estimates from the Daymet database (1-km grid) were linked to residential addresses. Average solar radiation exposures in the seven (SRAD7), 14 (SRAD14), and 30 days (SRAD30) before the home visit were calculated and categorized into quartiles (Q1-Q4). We used generalized linear mixed models to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for associations between solar radiation and depression/distress.

Results: Higher levels of SRAD7 were non-monotonically inversely associated with depression [PR_Vs.Q1 (95%CI): Q2 = 0.81 (0.68, 0.97), Q3 = 0.80 (0.65, 0.99), Q4 = 0.88 (0.69, 1.15)] and distress [PR_Vs.Q1 (95%CI): Q2 = 0.76 (0.58, 0.99), Q3 = 0.77 (0.57, 1.06), Q4 = 0.84 (0.58, 1.22)]. Elevated SRAD14 and SRAD30 appeared to be associated with decreasing PRs of distress. For example, for SRAD14, PRs were 0.86 (0.63-1.19), 0.80 (0.55-1.18), and 0.75 (0.48-1.17) for Q2-4 versus Q1. Associations with SRAD7 varied somewhat, though not significantly, by season with increasing PRs of distress in spring and summer and decreasing PRs of depression and distress in fall.

Impact statement: Previous research suffered from exposure misclassification, which impacts the validity of their conclusions. By leveraging high-resolution datasets and Gulf Long-term Follow-up Cohort, our findings support an association between increased solar radiation and fewer symptoms of mood disorders.

Background: Ambient air pollution has been linked to postpartum depression. However, few studies have investigated the effects of traffic-related NO_x on postpartum depression and whether any pregnancy-related factors might increase susceptibility.

Objectives: To evaluate the association between traffic-related NO_x and postpartum depressive and anxiety symptoms, and effect modification by pregnancy-related hypertension.

Methods: This study included 453 predominantly low-income Hispanic/Latina women in the MADRES cohort. Daily traffic-related NO_x concentrations by road class were estimated using the California LINE-source dispersion model (CALINE4) at participants' residential locations and averaged across pregnancy. Postpartum depressive and anxiety symptoms were evaluated by a validated questionnaire (Postpartum Distress Measure, PDM) at 1, 3, 6 and 12 months postpartum. Multivariate linear regressions were performed to estimate the associations at each timepoint. Interaction terms were added to the linear models to assess effect modification by hypertensive disorders of pregnancy (HDPs). Repeated measurement analyses were conducted by using mixed effect models.

Results: We found prenatal traffic-related NO_x was associated with increased PDM scores. Specifically, mothers exposed to an IQR (0.22 ppb) increase in NO_x from major roads had 3.78% (95% CI: 0.53-7.14%) and 5.27% (95% CI: 0.33-10.45%) significantly higher 3-month and 12-month PDM scores, respectively. Similarly, in repeated measurement analyses, higher NO_x from major roads was associated with 3.06% (95% CI: 0.43-5.76%) significantly higher PDM scores across the first year postpartum. Effect modification by HDPs was observed: higher freeway/highway and total NO_x among mothers with HDPs were associated with significantly higher PDM scores at 12 months postpartum compared to those without HDPs.

Impact: This study shows that prenatal traffic-related air pollution was associated with postpartum depressive and anxiety symptoms. The study also found novel evidence of greater susceptibility among women with HDPs, which advances the understanding of the relationships between air pollution, maternal cardiometabolic health during pregnancy and postpartum mental health. Our study has potential implications for clinical intervention to mitigate the effects of traffic-related pollution on postpartum mental health disorders. The findings can also offer valuable insights into urban planning strategies concerning the implementation of emission control measures and the creation of green spaces.

Background: While the Next Generation Air Transportation System (NextGen) in the United States optimizes flight patterns, it has led to the unintended consequence of increasing aircraft noise exposure in some communities near airports. Despite the evidence that chronic exposure to high noise levels produces detrimental health effects, potential adverse health consequences due to increased noise in the affected communities have not been adequately considered in aviation policy discussions.

Objective: We assessed the long-term health and associated economic burden of increased aircraft noise caused by NextGen near the Baltimore-Washington Thurgood Marshall International (BWI) airport in Maryland.

Methods: A probabilistic Markov model projected the incremental health and associated economic burden over 30, 20, and 10 years, comparing post-NextGen noise exposure levels to pre-NextGen levels. Health outcomes included cardiovascular disease (CVD), anxiety disorders, noise annoyance, and low birth weight (LBW). Noise exposure was categorized into four levels (<55 dB DNL, 55-60 dB DNL, 60-65 dB DNL, >65 dB DNL). A Monte Carlo simulation with 2000 iterations was run to obtain incremental burden estimates and uncertainty intervals. One-way sensitivity analyses for noise effect parameters were conducted.

Results: Increased aircraft noise exposure was estimated to produce (discounted) incremental mortality costs of $362 million, morbidity costs of $336 million, and losses of 15,326 Quality-Adjusted Life Years (QALYs) over the next 30 years. Sensitivity analyses revealed the greatest uncertainty for CVD outcomes.

Impact: NextGen is a system that can increase the operational efficiency of airports by optimizing flight patterns. While operational efficiency is beneficial in many ways, changes in flight patterns and volume can also produce noise pollution, a major public health concern that should be considered in policy decision-making. This study quantifies the long-term health and economic implications of increased aircraft noise exposure following the implementation of NextGen in communities near the Baltimore-Washington International Airport. Our findings underscore the importance of considering public health consequences of noise pollution.

Background: Microsensors have been used for the high-resolution particulate matter (PM) monitoring.

Objectives: This study applies PM and health microsensors with the objective of assessing the peak exposure, sources, and immediate health impacts of PM_2.5 and PM₁ in two Asian countries.

Methods: Exposure assessment and health evaluation were carried out for 50 subjects in 2018 and 2019 in Bandung, Indonesia and for 55 subjects in 2019 and 2020 in Kaohsiung, Taiwan. Calibrated AS-LUNG sets and medical-certified RootiRx® sensors were used to assess PM and heart-rate variability (HRV), respectively.

Results: Overall, the 5-min mean exposure of PM_2.5 and PM₁ was 30.4 ± 20.0 and 27.0 ± 15.7 µg/m³ in Indonesia and 14.9 ± 11.2 and 13.9 ± 9.8 µg/m³ in Taiwan, respectively. The maximum 5-min peak PM_2.5 and PM₁ exposures were 473.6 and 154.0 µg/m³ in Indonesia and 467.4 and 217.7 µg/m³ in Taiwan, respectively. Community factories and mosquito coil burning are the two most important exposure sources, resulting in, on average, 4.73 and 5.82 µg/m³ higher PM_2.5 exposure increments for Indonesian subjects and 10.1 and 9.82 µg/m³ higher PM_2.5 exposure for Taiwanese subjects compared to non-exposure periods, respectively. Moreover, agricultural waste burning and incense burning were another two important exposure sources, but only in Taiwan. Furthermore, 5-min PM_2.5 and PM₁ exposure had statistically significantly immediate impacts on the HRV indices and heart rates of all subjects in Taiwan and the scooter subjects in Indonesia with generalized additive mixed models. The HRV change for a 10 µg/m³ increase in PM_2.5 and PM₁ ranged from -0.9% to -2.5% except for ratio of low-high frequency, with greater impacts associated with PM₁ than PM_2.5 in both countries.

Impact statement: This work highlights the ability of microsensors to capture high peaks of PM_2.5 and PM₁, to identify exposure sources through the integration of activity records, and to assess immediate changes in heart rate variability for a panel of approximately 50 subjects in Indonesia and Taiwan. This study stands out as one of the few to demonstrate the immediate health impacts of peak PM, complementing to the short-term (days or weeks) or long-term effects (months or longer) assessed in most epidemiological studies. The technology/methodology employed offer great potential for researchers in the resource-limited countries with high PM_2.5 and PM₁ levels.