A modified Nordic prediction model of road traffic noise in a Taiwanese city with significant motorcycle traffic

Traffic noise modelling at intersections in mid-sized cities: an artificial neural network approach

Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342 h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.

Analysis of urban road traffic noise exposure of residential buildings in hong kong over the past decade

Introduction:

With the development of transportation system and the economy, the rapidly increasing number of automobiles brings the associated problem of road traffic noise, especially in metropolitan and densely populated high-rise cities like Hong Kong. In Hong Kong, approximately one million people are affected by severe road traffic noise. Excessive noise exposure is hazardous to the health and wellbeing of people and therefore has drawn progressively more attention in Hong Kong. The Calculation of Road Traffic Noise (CRTN) has been adopted as the sole tool to evaluate road traffic noise in the form of descriptor L_A10. The accuracy and suitability of the CRTN method for predicting road traffic noise in Hong Kong were evaluated in this study by comparing the prediction results and measured traffic noise levels. The results show that the CRTN method was able to provide adequate predictions with correlation coefficients of 0.8032 and 0.7626 between the predicted and measured L_A10 for 2007 and 2017 respectively. The predicted traffic noise levels on different floors of seven selected residential buildings in 2017 were compared with those predictions for the same buildings in 2007. The worsening traffic noise exposure in these residential buildings was analysed and some suggestions and counter-measures to alleviate the traffic noise problems are put forward. Since the situation of Hong Kong is an example of what may happen in other cities, the present longitudinal study of the road traffic noise in Hong Kong hopes to contribute to a better urban acoustic environment worldwide.

Context:

Excessive noise exposure is hazardous to the health and wellbeing of people and therefore has drawn progressively more attention in Hong Kong. The urban road traffic noise exposure of residential buildings in Hong Kong over the past decade has been analysed.

Aims:

This study aims to assess the road traffic noise exposure of residential buildings over the past decade.

Settings and Design:

Measurements of traffic noise levels at some selected residential buildings were first conducted in 2007, and then repeated at the same buildings in 2017.

Material and Methods:

The CRTN was adopted to predict the traffic noise levels based on the recorded traffic flow data.

Results:

The exposure of these buildings to road traffic noise is higher in 2017 than in 2007. The study illustrates that the deterioration of the urban acoustic environment may not be caused by an increased total number of vehicles, but that heavy vehicles are dominantly responsible for the increased traffic noise levels. Restriction of vehicle velocity for urban street canyons is useless for road traffic noise control.

Conclusions:

This study shows the deterioration of traffic noise levels is mainly due to the increased heavy vehicles instead of the increased total number of vehicles. The alleviation of traffic noise levels by velocity restriction may not be obvious for urban street canyons and may only work with a certain velocity range.

Evaluation of urban inland waterway traffic noise using a modified Nord 2000 prediction model

This study developed a prediction model for estimating urban inland waterway traffic noise emission level. The model based on the Scandinavian Nord 2000 method, which was modified by adding two categories of traffic flow, comprising light and heavy vessels, as well as vessel average speed to the calculating equations. Meanwhile, the influences of the water surface and embankment were also considered in the established model. Model verification was conducted using the data surveyed at the 30 sampling points of Danjinlicaohe Channel in Jiangsu Province of China. A high correlation was found between the predicted and measured noise values L_Aeq (Pearson correlation coefficient = 0.949, p < 0.01). And the mean difference between the predicted and measured noise values was 0.16 ± 1.28 dBA. The results showed that the proposed model had higher accuracy than the unmodified Nord 2000 method and can be applied for predicting vessel noise exposure level on inland waterway of China.

Prediction of L10 and Leq Noise Levels Due to Vehicular Traffic in Urban Area Using ANN and Adaptive Neuro-Fuzzy Interface System (ANFIS) Approach

Recently in urban areas, road traffic noise is one of the primary sources of noise pollution. Variation in noise level is impacted by the synthesis of traffic and the percentage of heavy vehicles. Presentation to high noise levels may cause serious impact on the health of an individual or community residing near the roadside. Thus, predicting the vehicular traffic noise level is important. The present study aims at the formulation of regression, an artificial neural network (ANN) and an adaptive neuro-fuzzy interface system (ANFIS) model using the data of observed noise levels, traffic volume, and average speed of vehicles for the prediction of L10 and Leq. Measured noise levels are compared to the noise levels predicted by the experimental model. It is observed that the ANFIS approach is more superior when compared to output given by regression and an ANN model. Also, there exists a positive correlation between measured and predicted noise levels. The proposed ANFIS model can be utilized as a tool for traffic direction and planning of new roads in zones of similar land use pattern.

Traffic-related air pollution associated with chronic kidney disease among elderly residents in Taipei City

The associations of air pollution with chronic kidney disease (CKD) have not yet been fully studied. We enrolled 8,497 Taipei City residents older than 65 years and calculated the estimated glomerular filtration rate (eGFR) using the Taiwanese Chronic Kidney Disease Epidemiology Collaboration equation. Proteinuria was assessed via dipstick on voided urine. CKD prevalence and risk of progression were defined according to the KDIGO 2012 guidelines. Land-use regression models were used to estimate the participants' one-year exposures to PM of different sizes and traffic-related exhaust, PM_2.5 absorbance, nitrogen dioxide (NO₂), and NO_x. Generalized linear regressions and logistic regressions were used to examine the associations of one-year air pollution exposures with eGFR, proteinuria, CKD prevalence and risk of progression. The results showed that the interquartile range (IQR) increments of PM_2.5 absorbance (0.4 × 10^-5/m) and NO₂ (7.0 μg/m³) were associated with a 1.07% [95% confidence interval (CI): 0.54-1.57] and 0.84% (95% CI: 0.37-1.32) lower eGFR, respectively; such relationships were magnified in subjects who had an eGFR >60 ml/min/1.73 m² or who were non-diabetic. Similar associations were also observed for PM₁₀ and PM_2.5-10. Two-pollutant models showed that PM₁₀ and PM_2.5 absorbance were associated with a lower eGFR. The odd ratios (ORs) of CKD prevalence and risk of progression also increased with exposures to PM_2.5 absorbance and NO_2. In summary, one-year exposures to traffic-related air pollution were associated with lower eGFR, higher CKD prevalence, and increased risk of CKD progression among the elderly population. Air pollution-related impaired renal function was stronger in non-CKD and non-diabetic subjects.

Predicting Aspergillus fumigatus exposure from composting facilities using a dispersion model: A conditional calibration and validation

Bioaerosols are released in elevated quantities from composting facilities and are associated with negative health effects, although dose-response relationships are unclear. Exposure levels are difficult to quantify as established sampling methods are costly, time-consuming and current data provide limited temporal and spatial information. Confidence in dispersion model outputs in this context would be advantageous to provide a more detailed exposure assessment. We present the calibration and validation of a recognised atmospheric dispersion model (ADMS) for bioaerosol exposure assessments. The model was calibrated by a trial and error optimisation of observed Aspergillus fumigatus concentrations at different locations around a composting site. Validation was performed using a second dataset of measured concentrations for a different site. The best fit between modelled and measured data was achieved when emissions were represented as a single area source, with a temperature of 29°C. Predicted bioaerosol concentrations were within an order of magnitude of measured values (1000-10,000CFU/m³) at the validation site, once minor adjustments were made to reflect local differences between the sites (r²>0.7 at 150, 300, 500 and 600m downwind of source). Results suggest that calibrated dispersion modelling can be applied to make reasonable predictions of bioaerosol exposures at multiple sites and may be used to inform site regulation and operational management.

Environmental impact of electric motorcycles: Evidence from traffic noise assessment by a building-based data mining technique

This study provided new evidence on the potential adoption of electric motorcycle (EM) as a cleaner alternative to gasoline-powered motorcycle. The effects of EM on human exposure to traffic noise were assessed in different urban areas with different traffic scenarios. The assessment was carried out by a developed building-based model system that took into account the contribution of motorcycle traffic. The results indicated that the EM could be an appealing solution to reduce the risk of human exposure to excessive high traffic noise in a motorcycle city. Particularly, in a historical urban area in which the total traffic volume was lower and motorcycle traffic was dominant, the proportion of noise levels meeting the standard of 70 dB(A) increased significantly from 12.2% to 41.9% when 100% of gasoline motorcycles in the real traffic scenario were replaced by EMs. On the other hand, in a modern urban area in which the total traffic volume was higher and traffic noise levels at majority of sites were higher than 75 dB(A), the proportion of noise levels above 75 dB(A) decreased significantly from 82.6% to 59.9%. Nevertheless, the effect of EM on improving the traffic noise compliance rate in the modern urban area was not significant and other policies or measures need to be sought.

International scale implementation of the CNOSSOS-EU road traffic noise prediction model for epidemiological studies

The EU-FP7-funded BioSHaRE project is using individual-level data pooled from several national cohort studies in Europe to investigate the relationship of road traffic noise and health. The detailed input data (land cover and traffic characteristics) required for noise exposure modelling are not always available over whole countries while data that are comparable in spatial resolution between different countries is needed for harmonised exposure assessment. Here, we assess the feasibility using the CNOSSOS-EU road traffic noise prediction model with coarser input data in terms of model performance. Starting with a model using the highest resolution datasets, we progressively introduced lower resolution data over five further model runs and compared noise level estimates to measurements. We conclude that a low resolution noise model should provide adequate performance for exposure ranking (Spearman's rank = 0.75; p < 0.001), but with relatively large errors in predicted noise levels (RMSE = 4.46 dB(A)).

Associations between Long-Term Air Pollutant Exposures and Blood Pressure in Elderly Residents of Taipei City: A Cross-Sectional Study

BACKGROUND

Limited information is available regarding long-term effects of air pollution on blood pressure (BP) and hypertension.

OBJECTIVE

We studied whether 1-year exposures to particulate matter (PM) and nitrogen oxides (NOx) were correlated with BP and hypertension in the elderly.

METHODS

We analyzed cross-sectional data from 27,752 Taipei City residents > 65 years of age who participated in a health examination program in 2009. Land-use regression models were used to estimate participants' 1-year exposures to particulate matter with aerodynamic diameter ≤ 10 μm (PM10), coarse particles (PM2.5-10), fine particles (≤ 2.5 μm; PM2.5), PM2.5 absorbance, NOx, and nitrogen dioxide (NO2). Generalized linear regressions and logistic regressions were used to examine the association between air pollution and BP and hypertension, respectively.

RESULTS

Diastolic BP was associated with 1-year exposures to air pollution, with estimates of 0.73 [95% confidence interval (CI): 0.44, 1.03], 0.46 (95% CI: 0.30, 0.63), 0.62 (95% CI: 0.24, 0.99), 0.34 (95% CI: 0.19, 0.50), and 0.65 (95% CI: 0.44, 0.85) mmHg for PM10 (10 μg/m3), PM2.5-10 (5 μg/m3), PM2.5 absorbance (10-5/m), NOx (20 μg/m3), and NO2 (10 μg/m3), respectively. PM2.5 was not associated with diastolic BP, and none of the air pollutants was associated with systolic BP. Associations of diastolic BP with PM10 and PM2.5 absorbance were stronger among participants with hypertension, diabetes, or a body mass index ≥ 25 kg/m2 than among participants without these conditions. One-year air pollution exposures were not associated with hypertension.

CONCLUSIONS

One-year exposures to PM10, PM2.5-10, PM2.5 absorbance, and NOx were associated with higher diastolic BP in elderly residents of Taipei.

Development of a traffic noise prediction model on inland waterway of China using the FHWA

Based on the local environmental standards, vessels types and traffic conditions, an inland waterway traffic noise prediction model was developed for use in China. This model was modified from the US FHWA model by adding the ground absorption and water surface attenuation correction terms to the governing equations. The parameters that were input into the equations, including traffic flow, vessel speed, distance from the center of the inland waterway to the receiver, position and height of the barriers and buildings, location of the receiver, type of ground, percentage of soft ground cover within the segment, and water surface conditions were re-defined. The model was validated by comparing the measured noise levels obtained at 33 sampling sites from Shugang Channel, Yanhe Channel and Danjinlicaohe Channel in China with the predicted values. The deviation between the predicted and measured noise levels within the range of ±1.5dB(A) was 81.8%. The mean difference between the predicted and measured noise levels was 0.15±1.75dB(A). However, the noise levels predicted developed model are generally higher than the measured levels. Overall, the comparison has proved that the developed method is of a high precision, and that it can be applied to estimate the traffic noise exposure level on inland waterway in China.

Estimation of populations exposed to road traffic noise in districts of Seoul metropolitan area of Korea

This study aims to model road traffic noise levels and estimate the human exposure at the 25 districts in the metropolitan Seoul, Republic of Korea. The SoundPLAN^® Version 7.1 software package was used to model noise levels and simulated road traffic noise maps were created. The people exposed to daytime/nighttime road traffic noise were also estimated. The proportions of the population exposed to road traffic noise in major cities in the EU were also estimated and compared. Eight (8) districts show the exceeded rate (percentage of the exposed population exceeding the daytime standard) of 20% or more, and eleven (11) districts show 10%-20% and six (6) districts show less than 10%, which indicates considerable variation among districts. Two districts (Nowon-gu and Yangcheon-gu) show the highest exposure rate during the daytime (35.2%). For nighttime noise levels, fourteen (14) districts show the exceeded rate (percentage of exposed population exceeding the nighttime standard) over 30%. The average percentages of the exposed population exceeding the daytime/nighttime standards in Seoul and the EU were 16.6%/34.8% and 13.0%/16.1%, respectively. The results show that road traffic noise reduction measures should urgently be taken for the nighttime traffic noise in Seoul. When the grid noise map and the 3-D façade noise map were compared, the 3-D façade noise map was more accurate in estimating exposed population in citywide noise mapping.