Noise estimation model development using high-resolution transportation and land use regression

Streets classification models by urban features for road traffic noise estimation

Road traffic is the primary source of environmental noise pollution in cities. This problem is also spreading due to inadequate urban expansion planning. Hence, integrating road traffic noise analysis into urban planning is necessary for reducing city noise in an effective, adaptable, and sustainable way. This study aims to develop a methodology that applies to any city for the stratification of urban roads by their functionality through only their urban features. It is intended to be a tool to cluster similar streets and, consequently, traffic noise to enable urban and transportation planners to support the reduction of people's noise exposure. Three multivariate ordered logistic regression statistical models (Model 1, 2, and 3) are presented that significantly stratify urban roads into five, four, and three categories, respectively. The developed models exhibit a McFadden pseudo-R2 between 0.5 and 0.6 (equivalent to R2 >0.8). The choice between Model 1 or 2 depends on the scale of the city. Model 1 is recommended for developed cities with an extensive road network, while Model 2 is most suitable in intermediate and growing cities. On the other hand, Model 3 could be applied at any city scale but focused on local management of transit routes and for designing acoustic sensor installations, urban soundwalks, and identification of quiet areas. Urban features related to road width and length, presence of transport infrastructure, and public transport routes are associated with increased traffic noise in all three models. These models prove useful for future action plans aimed at reducing noise through strategic urban planning.

Advancing Exposomic Research in Prenatal Respiratory Disease Programming

Disease programming reflects interactions between genes and the environment. Unlike the genome, environmental exposures and our response to exposures change over time. Starting in utero, the respiratory system and related processes develop sequentially in a carefully timed cascade, thus effects depend on both exposure dose and timing. A multitude of environmental and microbial exposures influence respiratory disease programming. Effects result from toxin-induced shifts in a host of molecular, cellular, and physiologic states and their interacting systems. Moreover, pregnant women and the developing child are not exposed to a single toxin, but to complex mixtures.

Spatial modelling and inequalities of environmental noise in Accra, Ghana

Noise pollution is a growing environmental health concern in rapidly urbanizing sub-Saharan African (SSA) cities. However, limited city-wide data constitutes a major barrier to investigating health impacts as well as implementing environmental policy in this growing population. As such, in this first of its kind study in West Africa, we measured, modelled and predicted environmental noise across the Greater Accra Metropolitan Area (GAMA) in Ghana, and evaluated inequalities in exposures by socioeconomic factors. Specifically, we measured environmental noise at 146 locations with weekly (n = 136 locations) and yearlong monitoring (n = 10 locations). We combined these data with geospatial and meteorological predictor variables to develop high-resolution land use regression (LUR) models to predict annual average noise levels (LAeq24hr, Lden, Lday, Lnight). The final LUR models were selected with a forward stepwise procedure and performance was evaluated with cross-validation. We spatially joined model predictions with national census data to estimate population levels of, and potential socioeconomic inequalities in, noise levels at the census enumeration-area level. Variables representing road-traffic and vegetation explained the most variation in noise levels at each site. Predicted day-evening-night (Lden) noise levels were highest in the city-center (Accra Metropolis) (median: 64.0 dBA) and near major roads (median: 68.5 dBA). In the Accra Metropolis, almost the entire population lived in areas where predicted Lden and night-time noise (Lnight) surpassed World Health Organization guidelines for road-traffic noise (Lden <53; and Lnight <45). The poorest areas in Accra also had significantly higher median Lden and Lnight compared with the wealthiest ones, with a difference of ∼5 dBA. The models can support environmental epidemiological studies, burden of disease assessments, and policies and interventions that address underlying causes of noise exposure inequalities within Accra.

Reclaiming independence in spatial-clustering datasets: A series of data-driven spatial weights matrices

Most spatial models include a spatial weights matrix (W) derived from the first law of geography to adjust the spatial dependence to fulfill the independence assumption. In various fields such as epidemiological and environmental studies, the spatial dependence often shows clustering (or geographic discontinuity) due to natural or social factors. In such cases, adjustment using the first‐law‐of‐geography‐based W might be inappropriate and leads to inaccuracy estimations and loss of statistical power. In this work, we propose a series of data‐driven Ws (DDWs) built following the spatial pattern identified by the scan statistic, which can be easily carried out using existing tools such as SaTScan software. The DDWs take both the clustering (or discontinuous) and the intuitive first‐law‐of‐geographic‐based spatial dependence into consideration. Aiming at two common purposes in epidemiology studies (ie, estimating the effect value of explanatory variable X and estimating the risk of each spatial unit in disease mapping), the common spatial autoregressive models and the Leroux‐prior‐based conditional autoregressive (CAR) models were selected to evaluate performance of DDWs, respectively. Both simulation and case studies show that our DDWs achieve considerably better performance than the classic W in datasets with clustering (or discontinuous) spatial dependence. Furthermore, the latest published density‐based spatial clustering models, aiming at dealing with such clustering (or discontinuity) spatial dependence in disease mapping, were also compared as references. The DDWs, incorporated into the CAR models, still show considerable advantage, especially in the datasets for common diseases.

Predicting traffic noise using land-use regression-a scalable approach

BACKGROUND

In modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas.

OBJECTIVE

Based on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model.

METHODS

Compliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day-evening-night noise level indicator L_den. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas.

RESULTS

The experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (L_den) variability (R² = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A).

SIGNIFICANCE

This data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Application of land use regression to map environmental noise in Shanghai, China

BACKGROUND

Urban environment noise has been linked with wide adverse effects on health; however, noise epidemiological researches are hindered by the lack of large-scale population-based exposure assessment.

OBJECTIVE

We aimed to measure noise levels over multiple seasons and to establish an LUR model to assess the spatial variability of intra-urban noise and identify its potential sources in Shanghai, China.

METHODS

Forty-minute (L_{Aeq, 40 min}) measurements of environmental noise were collected at 144 fixed sites, and each was visited three times (morning, afternoon, and evening) in winter, spring, and summer in 2019. Noise measurements were then integrated with land-use types, road networks, socioeconomic variables, and geographic information systems to construct LUR models. Ten-fold cross-validation was used to test the model performance.

RESULTS

A total of 1296 measurements and 29 predicting variables were used to estimate the spatial variation in environmental noise. The annual mean (±standard deviation) of L_{Aeq, 40min}, was 62 ± 8 dB (A). Significant variations were observed among monitoring sites but not between seasons or time of day. The LUR model explained 79% of the spatial variability of the noise, and the R² of the ten-fold cross-validation was 0.75. The most contributory predictors of noise level were road-related variables all within the 50-m buffers, followed by urban area within a 50-m buffer, total area of buildings within a 1000-m buffer, and number of restaurant clusters within a 50-m buffer. Farmland area within a 100-m buffer was the only negative variable in the model. A 50-m resolution noise prediction map was produced and suggested high noise level in urban areas and near traffic arteries.

CONCLUSION

LUR can be a robust method for reflecting noise variability in megacities such as Shanghai and may provide an efficient solution for noise exposure assessment in areas where noise maps are not available.

Community daytime noise pollution and socioeconomic differences in Chicago, IL

Environmental noise may affect hearing and a variety of non-auditory disease processes. There is some evidence that, like other environmental hazards, noise may be differentially distributed across communities based on socioeconomic status. We aimed to a) predict daytime noise pollution levels and b) assess disparities in daytime noise exposure in Chicago, Illinois. We measured 5-minute daytime noise levels (L_{eq, 5-min}) at 75 randomly selected sites in Chicago in March, 2019. Geographically-based variables thought to be associated with noise were obtained, and used to fit a noise land-use regression model to estimate the daytime environmental noise level at the centroid of the census blocks. Demographic and socioeconomic data were obtained from the City of Chicago for the 77 community areas, and associations with daytime noise levels were assessed using spatial autoregressive models. Mean sampled noise level (L_{eq, 5-min}) was 60.6 dBA. The adjusted R² and root mean square error of the noise land use regression model and the validation model were 0.60 and 4.67 dBA and 0.51 and 5.90 dBA, respectively. Nearly 75% of city blocks and 85% of city communities have predicted daytime noise level higher than 55 dBA. Of the socioeconomic variables explored, only community per capita income was associated with mean community predicted noise levels, and was highest for communities with incomes in the 2^nd quartile. Both the noise measurements and land-use regression modeling demonstrate that Chicago has levels of environmental noise likely contributing to the total burden of environmental stressors. Noise is not uniformly distributed across Chicago; it is associated with proximity to roads and public transportation, and is higher among communities with mid-to-low incomes per capita, which highlights how socially and economically disadvantaged communities may be disproportionately impacted by this environmental exposure.

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.

Noise Estimation Using Road and Urban Features

Noise pollution must be considered to achieve sustainable cities because current levels of exposure to environmental noise are a considerable risk to the health and quality of life of citizens. Urban features and sound levels were registered in 150 streets in the Chilean cities of Talca and Valdivia to analyze the relationship between both types of variables. Urban variables related to street location, urban land use, street geometry, road traffic control, and public and private transportation showed very significant correlations with the noise levels, and multiple regression models were developed from these variables for each city. Models using only urban variables in Valdivia and Talca explained 71% and 73%, respectively, of the variability of noise. The prediction error was similar in the different types of urban roads and did not exhibit significant differences between models developed in different cities. The urban models developed in one city could, therefore, be used in other similar cities. Considering the usefulness of these variables in urban planning, these models can be a useful tool for urban planners and decision-makers to implement action plans regarding noise pollution.

Application of land-use regression models to estimate sound pressure levels and frequency components of road traffic noise in Taichung, Taiwan

Few studies have applied land-use regression to predict road traffic noise exposure, and there are few predictive models for different frequencies. This study aimed to measure 24-h average road traffic noise levels and to analyze the frequency components over one year to establish land-use regression models of noise exposure. Fifty monitoring stations were set up to conduct 3 measurements for A-weighted equivalent sound pressure levels over 24 h (L_eq,24h) and night equivalent sound pressure levels (L_night), as well as octave-band analyses, during the 2013-2014 period. Noise measurements were integrated with land-use types, road and traffic information, meteorological data and geographic information systems to construct land-use regression models. Leave-one-out cross-validation was performed to test the validity of the predictive models. The annual means of L_eq,24h and L_night were 66.4 ± 4.7 A-weighed decibels (dBA) and 62.1 ± 6.0 dBA, respectively. Octave-band frequency analyses revealed that the highest means over 24 h and at night were 61.4 ± 5.3 decibels (dB) and 56.7 ± 6.6 dB (both at 1000 Hz), respectively. The model-explained variance (R²) of the full-frequency noise was 0.83 for L_eq,24h and 0.79 for L_night. The R² values for octave-band-frequency noise ranged from 0.67 to 0.88 for L_eq,24h and 0.65 to 0.85 for L_night, with the highest R² at 250 Hz for L_eq,24h and at 125 Hz for L_night. The differences between the model R² and the leave-one-out cross-validation R² ranged from 5% to 15% for both L_eq,24h and L_night at all frequencies. In the validation, the root mean squared error was 2.09 dBA and 2.80 dBA for the full-frequency L_eq,24 and L_night, respectively, and ranged from 1.89 to 2.62 dB and from 2.51 to 3.28 dB for the octave-band-frequency L_eq,24h and L_night, respectively. This study observed that the annual means of the measured L_eq,24h and L_night in Taichung were both above 60 dBA and had the highest level at 1000 Hz. The developed land-use regression models of L_eq,24 and L_night both had good predictive capacity for the full frequency spectrum and within octave bands and can therefore be applied for epidemiological studies.

Façades, floors and maps - Influence of exposure measurement error on the association between transportation noise and myocardial infarction

BACKGROUND

Epidemiological research on transportation noise uses different exposure assessment strategies based on façade point estimates or regulatory noise maps. The degree of exposure measurement error and subsequent potentially biased risk estimates related to exposure definition is unclear. We aimed to evaluate associations between transportation noise exposure and myocardial infarction (MI) mortality considering: assumptions about residential floor, façade point selection (loudest, quietest, nearest), façade point vs. noise map estimates, and influence of averaging exposure at coarser spatial scales (e.g. in ecological health studies).

METHODS

L_den from the façade points were assigned to >4 million eligible adults in the Swiss National Cohort for the best match residential floor (reference), middle floor, and first floor. For selected floors, the loudest and quietest exposed façades per dwelling, plus the nearest façade point to the residential geocode, were extracted. Exposure was also assigned from 10 × 10 m noise maps, using "buffers" from 50 to 500 m derived from the maps, and by aggregating the maps to larger areas. Associations between road traffic and railway noise and MI mortality were evaluated by multi-pollutant Cox regression models, adjusted for aircraft noise, NO₂ and socio-demographic confounders, following individuals from 2000 to 2008. Bias was calculated to express differences compared to the reference.

RESULTS

Hazard ratios (HRs) for the best match residential floor were 1.05 (1.02-1.07) and 1.03 (1.01-1.05) per IQR (11.3 and 15.0 dB) for road traffic and railway noise, respectively. In most situations, comparing the alternative exposure definitions to this reference resulted in attenuated HRs. For example, assuming everyone resided on the middle or everyone on first floor introduced little bias (%Bias in excess risk: -1.9 to 4.4 road traffic and -4.4 to 10.7 railway noise). Using the noise grids generated a bias of approximately -26% for both sources. Averaging the maps at a coarser spatial scale led to bias from -19.4 to -105.1% for road traffic and 17.6 to -34.3% for railway noise and inflated the confidence intervals such that some HRs were no longer statistically significant.

CONCLUSION

Changes in spatial scale introduced more bias than changes in residential floor. Use of noise maps to represent residential exposure may underestimate noise-induced health effects, in particular for small-scale heterogeneously distributed road traffic noise in urban settings.