Micro-scale modelling of the urban wind speed for air pollution applications

Modelling wind speeds in urban areas have many applications e.g. in relation to assessment of wind energy, modelling air pollution, and building design and engineering. Models for extrapolating the urban wind speed exist, but little attention has been paid to the influence of the upwind terrain and the foundations for the extrapolation schemes. To analyse the influence of the upwind terrain and the foundations for the extrapolation of the urban wind speed, measurements from six urban and non-urban stations were explored, and a model for the urban wind speed with and without upwind influence was developed and validated. The agreement between the wind directions at the stations is found to be good, and the influence of atmospheric stability, horizontal temperature gradients, land-sea breeze, temperature, global radiation and Monin-Obukhov Length is found to be small, although future work should explore if this is valid for other urban areas. Moreover, the model is found to perform reasonably well, but the upwind influence is overestimated. Areas of model improvement are thus identified. The upwind terrain thus influences the modelling of the urban wind speed to a large extent, and the fundamental assumptions for the extrapolation scheme are fulfilled for this specific case.Other Information Published in: Scientific Reports License: https://creativecommons.org/licenses/by/4.0See article on publisher's website: http://dx.doi.org/10.1038/s41598-019-50033-2</p

Hyperlocal Air Pollution in London: Validating Low-Cost Sensors for Mobile Measurements from Vehicles

This study assesses low-cost sensors (LCS) for the mobile monitoring of air quality, which has thus far been scarcely investigated. NO2 and PM2.5 were measured using LCS and higher-grade instruments while driving across various environments in London (943 km) and northern Europe (2923 km), including urban areas, motorways, and tunnels. The data were classified according to the environment where the measurements were carried out, and the performances of LCS and higher-grade instruments were compared. Results indicate that the performances of the sensors were influenced by the rate of change in pollutant concentration in different environments and not by vehicle speed. Excluding tunnel environments, overall, the particulate matter sensors correlated better with their higher-grade instrument than the electrochemical (EC) sensors, with R2 values from 0.90–0.96 in the different environments, compared with 0.39–0.72 for the EC sensors. Tunnels presented a unique opportunity to test the time response of the systems, given the rapid change in concentration upon entering, and all sensors showed limited response times. This is the first time that EC NO2 sensors have been rigorously tested against reference monitors while mobile. Their absolute measurements appear unaffected by movement; however, their time resolution may not be high enough for mobile monitoring in highly variable environments

The spline function is adjusted for age, sex, consumption of fruit and vegetables, employment in the chemical industry for at least one year and traffic-related air pollution.

<p>The exposure distribution of average residential radon is marked on the x-axis. The spline function can be interpreted as the exposure-response association. The difference between two points on the y-axis on the curve is interpreted as the difference in loge(IRR) for the corresponding difference in exposure, which can be read on the x-axis between the same two points.</p

Incidence rate ratios (95% CI) for primary brain tumour risk associated with the residential radon concentrations.

a<p>From 1 January 1971 until censoring, with inclusion of a 10 year latency period. The cut-off points between exposure groups were the 25^th, 50^th and 75^th percentiles for all participants.</p>b<p>Analyses based on 51,674 cohort members and 121 brain tumours.</p>c<p>Adjusted for age by using it as the underlying time scale in the Cox model and sex.</p>d<p>Adjusted for consumption of fruit and vegetables, employment in the chemical industry for at least one year and traffic (time-weighted average NO_x exposure between 1971 and the censoring date).</p>e<p>Adjusted for employment status, schooling and marital status. Due to exclusion of cohort members with missing value in any covariate, the number of persons is identical in the crude and the adjusted analyses.</p

Adjusted^a incidence rate ratios for primary brain tumour in association with a 100 Bq/m³ increase in domestic radon^b within strata of NO_x at the residential address.

a<p>We adjusted the analyses for age (underlying time scale), sex, employment in the chemical industry for at least one year and consumption of fruit and vegetables.</p>b<p>Radon exposure was entered as a continuous variable in all models as the time-weighted average concentration at residences from 1. January 1971 until censoring.with inclusion of a 10 year latency period.</p>c<p>Test of the null hypothesis that the linear trends are identical, for Wald test for interaction.</p>d<p>Time-weighted average concentration for NO_x.</p

Crude and adjusted RRs for leukemia in association with distance to nearest power line.

a<p>The crude model.</p>b<p>Adjusted for socioeconomic status and urbanization.</p>c<p>Adjusted for the same as model 2 and for maternal age and birth order.</p>d<p>Adjusted for the same as model 3 and for domestic radon and air pollution.</p><p>Crude and adjusted RRs for leukemia in association with distance to nearest power line.</p

Potential confounders by case-control status and distance to nearest power line.

1<p>Cut-point is the 50^th and 90^th percentile.</p><p>Potential confounders by case-control status and distance to nearest power line.</p

The joint effects of distance to nearest power line and domestic radon and air pollution, respectively, on leukemia.

1<p>Cut-point is the median.</p>2<p>The adjusted analysis includes following potential confounders: socioeconomic status, urbanization, maternal age, birth order and air pollution.</p>3<p>The adjusted analysis includes following potential confounders: socioeconomic status, urbanization, maternal age, birth order and domestic radon.</p><p>The joint effects of distance to nearest power line and domestic radon and air pollution, respectively, on leukemia.</p

Characteristics of all study participants, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM).

<p>^aRow percentages. All other percentages are column percentages.</p><p>^bTime-weighted average radon at the residencies for the period 1 January 1971, to censoring date.</p><p>Characteristics of all study participants, basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and malignant melanoma (MM).</p

Association between time-weighted average radon exposure^# and basal cell carcinoma, squamous cell carcinoma and malignant melanoma among 51,445 Diet Cancer Health cohort participants.

<p>^aAdjusted for age (underlying time scale) and sex.</p><p>^bAdjusted for skin reaction to sunlight, degree of freckles, degree of nevi, BMI, school attendance, area-level socio-economic status, leisure time physical activities including sports, cycling, walking and gardening as well as outdoor occupation (farming, mining, quarrying, roofing or asphalt road work) and mean daily hours of bright sunshine at the level of municipality of each residence.</p><p>Due to exclusion of cohort members with missing value in any covariate, the number of persons is identical in the crude and the adjusted analyses.</p><p>^#Radon exposure was entered as a continuous variable in all models as the time-weighted average at residences from 1 January 1971 until censoring.</p><p>Association between time-weighted average radon exposure<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135642#t002fn004" target="_blank">^#</a> and basal cell carcinoma, squamous cell carcinoma and malignant melanoma among 51,445 Diet Cancer Health cohort participants.</p