Reductions in community noise levels in vancouver, Canada, during pandemic lockdown and association with land cover type

BACKGROUND

Urban transportation noise is a major public concern because of its adverse effects on health. The determinants of urban noise exposure have not been widely explored but the "natural experiment" presented by the COVID-19 lockdowns presented a unique opportunity. This study examined the relationship between environmental characteristics and urban noise pollution during the COVID-19 related lockdown in Metro Vancouver, Canada, from March 21st to May 18th, 2020.

METHODS

We used noise exposure data from the Vancouver International Airport (YVR) noise management program, comparing the noise levels during "Phase One" of the COVID-19 lockdown in 2020 to the corresponding time period in 2019 from 21 Noise Monitoring Terminals (NMTs) located throughout Metro Vancouver. We modelled the relationship between the change in noise level and the physical NMT environments, including land cover, and total length of roads at four different time periods (24Hr, daytime, evening and nighttime) and within three different buffer zones (100 m, 250 m, and 500 m).

RESULTS

Of 59,472 hourly measurements of community noise, the 24-h noise level was reduced by an average of 2.20 dBA between 2019 and 2020. Higher proportions of greenspace, barren areas, and soil-cover around NMTs resulted in stronger noise reductions and higher density of building, pavement, and water weakened the amount of noise reduction. Proximity of high-volume traffic roads (highways) were associated with weaker noise reduction.

CONCLUSION

The COVID-19 related lockdown was associated with reduced noise in Metro Vancouver, and the relative reduction depended on the types of the environment surrounding the NMT. Future research on the effects of urban environmental characteristics on geographic inequality in noise levels and health consequences of the COVID-19 related lockdown is merited.

Association between community noise and children's cognitive and behavioral development: A prospective cohort study

BACKGROUND

Noise exposure has been associated with adverse cognitive and behavioral outcomes in children, but evidence on longitudinal associations between community noise and child development in low- and middle-income countries is rare. We investigated associations between community noise and behavioral and cognitive development in preschool children in São Paulo.

METHODS

We linked child development data from the São Paulo Western Region Birth Cohort with average (Lden) and night-time (Lnight) community noise exposure at children's home, estimated by means of a land use regression model using various predictors (roads, schools, greenness, residential and informal settlements). Outcomes were the Strengths and Difficulties Questionnaire (SDQ) and Regional Project on Child Development Indicators (PRIDI) at 3 years of age and the Child Behavior Checklist (CBCL) and International Development and Early Learning Assessment (IDELA) at 6 years of age. We investigated the relationship between noise exposure and development using cross-sectional and longitudinal regression models.

RESULTS

Data from 3385 children at 3 years of age and 1546 children at 6 years of age were analysed. Mean Lden and Lnight levels were 70.3 dB and 61.2 dB, respectively. In cross-sectional analyses a 10 dB increase of Lden above 70 dB was associated with a 32% increase in the odds of borderline or abnormal SDQ total difficulties score (OR = 1.32, 95% CI: 1.04; 1.68) and 0.72 standard deviation (SD) increase in the CBCL total problems z-score (95% CI: 0.55; 0.88). No cross-sectional association was found for cognitive development. In longitudinal analyses, each 10 dB increase was associated with a 0.52 SD increase in behavioral problems (95% CI: 0.28; 0.77) and a 0.27 SD decrease in cognition (95%-CI: 0.55; 0.00). Results for Lnight above 60 dB were similar.

DISCUSSION

Our findings suggest that community noise exposure above Lden of 70 dB and Lnight of 60 dB may impair behavioral and cognitive development of preschool children.

Land use regression modelling of community noise in São Paulo, Brazil

Noise pollution has negative health consequences, which becomes increasingly relevant with rapid urbanization. In low- and middle-income countries research on health effects of noise is hampered by scarce exposure data and noise maps. In this study, we developed land use regression (LUR) models to assess spatial variability of community noise in the Western Region of São Paulo, Brazil.We measured outdoor noise levels continuously at 42 homes once or twice for one week in the summer and the winter season. These measurements were integrated with various geographic information system variables to develop LUR models for predicting average A-weighted (dB(A)) day-evening-night equivalent sound levels (L_den) and night sound levels (L_night). A supervised mixed linear regression analysis was conducted to test potential noise predictors for various buffer sizes and distances between home and noise source. Noise exposure levels in the study area were high with a site average L_den of 69.3 dB(A) ranging from 60.3 to 82.3 dB(A), and a site average L_night of 59.9 dB(A) ranging from 50.7 to 76.6 dB(A). LUR models had a good fit with a R² of 0.56 for L_den and 0.63 for L_night in a leave-one-site-out cross validation. Main predictors of noise were the inverse distance to medium roads, count of educational facilities within a 400 m buffer, mean Normalized Difference Vegetation Index (NDVI) within a 100 m buffer, residential areas within a 50 m (L_den) or 25 m (L_night) buffer and slum areas within a 400 m buffer. Our study suggests that LUR modelling with geographic predictor data is a promising and efficient approach for noise exposure assessment in low- and middle-income countries, where noise maps are not available.

Manuscript title: Long─term residential exposure to environmental/transportation noise and the incidence of myocardial infarction

BACKGROUND

Cardiovascular effects of environmental noise are a growing concern. However, the evidence remains largely limited to the association between road traffic noise and hypertension and coronary heart diseases.

OBJECTIVES

To investigate the association between long-term residential exposure to environmental/transportation noise and the incidence of myocardial infarction (MI) in the adult population living in Montreal.

METHODS

An open cohort of adults aged 45 years old and over, living on the island of Montreal and free of MI before entering the cohort was created for the years 2000-2014 with the Quebec Integrated Chronic Disease Surveillance System; a systematic surveillance system from the Canadian province of Quebec starting in 1996. Residential noise exposure was calculated in three ways: 1) total ambient noise levels estimated by Land use regression (LUR) models; 2) road traffic noise estimated by a noise propagation model CadnaA and 3) distances to transportation sources (roads, airport, railways). Incident MI was based on diagnostic codes in hospital admission records. Cox models with time-varying exposures (age as the time axis) were used to estimate the associations with various adjustments (material deprivation indicator, calendar year, nitrogen dioxide, stratification for sex). Indirect adjustment based on ancillary data for smoking was performed.

RESULTS

1,065,414 individuals were followed (total of 9,000,443 person-years) and 40,718 (3.8%) developed MI. We found positive associations between total environmental noise, estimated by LUR models and the incidence of MI. Total noise LUR levels ranged from ~44 to ~79 dBA and varied slightly with the metric used. The adjusted hazard ratios (HRs) (also adjusted for smoking) were 1.12 (95% Confidence Intervals [CI]: 1.08-1.15), 1.11 (95%CI: 1.07-1.14) and 1.10 (95%CI: 1.06-1.14) per 10 dBA noise levels increase respectively in Level Accoustic equivalent 24 h (LAeq24 h), Level day-evening-night (Lden) and night level (Lnight). We found a borderline negative association between road noise levels estimated with CadnaA and MI (HR: 0.99 per 10 dBA; 95%CI: 0.98-1.00). Distances to major roads and highways were not associated with MI while the proximity to railways was positively associated with MI (HR for ≤100 vs > 1000 m: 1.07; 95%CI: 1.01-1.14). A negative association was found with the proximity to the airport noise exposure forecast (NEF25); HR (<1 vs >1000 m) = 0.88 (95%CI: 0.81-0.96).

CONCLUSIONS

These associations suggest that exposure to total environmental noise at current urban levels may be related to the incidence of MI. Additional studies with more accurate road noise estimates are needed to explain the counterintuitive associations with road noise and specific transportation sources.

Community noise exposure and annoyance, activity interference, and academic achievement among university students

Background:

Noise annoyance and effects on academic performance have been investigated for primary and secondary school students but comparatively little work has been conducted with university students who generally spend more time in dormitories or accommodation for their self-study.

Objective:

To determine, using a socio-acoustic approach involving face-to-face interviews and actual noise measurements, the effect of various community noise sources on student activities in accommodation both inside and outside a university precinct and also relationships with cumulative grade point average (GPA).

Materials and Methods:

The study sample comprised a student group resident off-campus (n = 450) and a control group resident in dormitories on-campus (n = 336). Noise levels [L_A (dB)] were measured at both locations according to International Organization for Standardization standards. The extent of community noise interference with the student activities was examined with bivariate and stratified analyses and results presented as Mantel–Haenszel weighted odds ratios (OR_MH) with 95% confidence intervals. Binary logistic regression was employed to assess the association between noise-disturbed student activities and dichotomized GPA values and derive odds ratios (ORs) for these associations.

Results:

Measured noise levels were all significantly (P < 0.05) higher for off-campus students. This was not reflected in the interviewed students’ subjective perceptions of how “noisy” their respective environments were. The off-campus student cohort was, however, more annoyed by all community noise categories (P < 0.001) except road traffic noise. For impact on specific student activities, the largest differences between on- and off-campus students were found for telephone and personal communication regardless of the type of community noise. There was no significant difference in the relationships between perceived annoyance due to community noise categories and cumulative GPA in the off-campus group compared to those for on-campus residents with OR_MH values ranging from 1.049 to 1.164. The most important noise-impacted factors affecting off-campus students’ cumulative GPA were reading and mental tasks (OR = 2.801). Rest disturbance had a positive influence on cumulative GPA for on-campus students.

Conclusion:

These results provide support that various contemporary community noise sources affect university students’ activities and possibly influence their educational achievement as well.

Residential road traffic noise as a risk factor for hypertension in adults: Systematic review and meta-analysis of analytic studies published in the period 2011-2017

Multiple cross-sectional studies indicated an association between hypertension and road traffic noise and they were recently synthetized in a WHO systematic evidence review. However, recent years have seen a growing body of high-quality, large-scale research, which is missing from the WHO review. Therefore, we aimed to close that gap by conducting an updated systematic review and meta-analysis on the exposure-response relationship between residential road traffic noise and the risk of hypertension in adults. Studies were identified by searching MEDLINE, EMBASE, the Internet, conference proceedings, reference lists, and expert archives in English, Russian, and Spanish through August 5, 2017. The risk of bias for each extracted estimate and the overall quality of evidence were evaluated using a list of predefined safeguards against bias related to different study characteristics and the Grading of Recommendations Assessment, Development and Evaluation system, respectively. The inverse variance heterogeneity (IVhet) model was used for meta-analysis. The possibility of publication bias was evaluated by funnel and Doi plots, and asymmetry in these was tested with Egger's test and the Luis Furuya-Kanamori index, respectively. Sensitivity analyses included leave-one-out meta-analysis, subgroup meta-analysis with meta-regressions, and non-linear exposure-response meta-analysis. Based on seven cohort and two case-control studies (n = 5 514 555; 14 estimates; L_den range ≈ 25-90 dB(A)), we found "low" evidence of RR _{per 10 dB(A)} = 1.018 (95% CI: 0.984, 1.053), moderate heterogeneity (I² = 46%), and no publication bias. In the subgroup of cohort studies, we found "moderate" evidence of RR _{per 10 dB(A)} = 1.018 (95% CI: 0.987, 1.049), I² = 31%, and no publication bias. In conclusion, residential road traffic noise was associated with higher risk of hypertension in adults, but the risk was lower than previously reported in the systematic review literature.

Exploring nighttime road traffic noise: A comprehensive predictive surface for Toronto, Canada

Road traffic noise can adversely impact the health of city residents, particularly when it occurs at night. The objective of this study was to evaluate nighttime traffic ambient noise in Toronto, Canada using measured and model-estimated noise levels. Road traffic noise was measured at 767 locations over 3 seasonal sampling campaigns between June 2012 and October 2013 to fully capture noise variability in Toronto. Temporal and campaign-specific spatial models, developed using the noise measurements, were used to build a final predictive surface. The surface was capable of estimating noise across the city over a 24-hr time frame. Measured and surface-estimated noise levels were compared with guidelines from the World Health Organization and the Province of Ontario to identify areas where noise may pose a health risk. Measured mean nighttime noise in Toronto exceeded World Health Organization (40 dBA) guidelines and mean daytime noise exceeded provincial (55 dBA) guidelines. The final predictive surface, incorporating spatial variables and daily cycles in noise levels, provides noise estimates geocoded for the entire study area. This tool could be used for epidemiological studies and to inform noise mitigation efforts. Based on surface-estimated noise levels during the quietest time of night (2 a.m.-2:30 a.m.), 100% of Toronto has nighttime noise exceeding 40 dBA (mean = 57 dBA, range = 49-110 dBA). A predictive surface was developed to estimate geocoded noise levels and facilitate further study of noise in Toronto. This tool can be used to assess road traffic noise, particularly at night, as an environmental health hazard.

Assessment of traffic-related noise in three cities in the United States

BACKGROUND

Traffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities.

METHODS

Noise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration's Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input.

RESULTS

We found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input.

CONCLUSIONS

Ambient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics.