Short-term Monitoring Campaign Research Articles

On urban microclimate spatial-temporal dynamics: Evidence from the integration of fixed and wearable sensing and mapping techniques

Urban Heat Island (UHI) is acknowledged to generate harmful consequences on human health, and it is one of the main anthropogenic challenges to face in modern cities. Due to the urban dynamic complexity, a full microclimate decoding is required to design tailored mitigation strategies for reducing heat-related vulnerability. This study proposes a new method to assess intra-urban microclimate variability by combining for the first time two dedicated monitoring systems consisting of fixed and mobile techniques. Data from three fixed weather stations were used to analyze long-term trends, while mobile devices (a vehicle and a wearable) were used in short-term monitoring campaigns conducted in summer and winter to assess and geo-locate microclimate spatial variations. Additionally, data from mobile devices were used as input for Kriging interpolation in the urban area of Florence (Italy) as case study. Mobile monitoring sessions provided high-resolution spatial data, enabling the detection of hyperlocal variations in air temperature. The maximum air temperature amplitudes were verified with the wearable system: 3.3 °C in summer midday and 4.3 °C in winter morning. Physiological Equivalent Temperature (PET) demonstrated to be similar when comparing green areas and their adjacent built-up zone, showing up the microclimate mitigation contribution of greenery in its surrounding. Results also showed that mixing the two data acquisition and varied analysis techniques succeeded in investigating the UHI and the site-specific role of potential mitigation actions. Moreover, mobile dataset was reliable for elaborating maps by interpolating the monitored parameters. Interpolation results demonstrated the possibility of optimizing mobile monitoring campaigns by focusing on targeted streets and times of day since interpolation errors increased by 10% only with properly reduced and simplified input samples. This allowed an enhanced detection of the site-specific granularity, which is important for urban planning and policymaking, adaptation, and risk mitigation actions to overcome the UHI and anthropogenic climate change effects.

Reliability Testing of a Low-Cost, Multi-Purpose Arduino-Based Data Logger Deployed in Several Applications Such as Outdoor Air Quality, Human Activity, Motion, and Exhaust Gas Monitoring.

This contribution shows the possibilities of applying a low-cost, multi-purpose data logger built around an Arduino Mega 2560 single-board computer. Most projects use this kind of hardware to develop single-purpose data loggers. In this work, a data logger with a more general hardware and software architecture was built to perform measurement campaigns in very different domains. The wide applicability of this data logger was demonstrated with short-term monitoring campaigns in relation to outdoor air quality, human activity in an office, motion of a journey on a bike, and exhaust gas monitoring of a diesel generator. In addition, an assessment process and corresponding evaluation framework are proposed to assess the credibility of low-cost scientific devices built in-house. The experiences acquired during the development of the system and the short measurement campaigns were used as inputs in the assessment process. The assessment showed that the system scores positively on most product-related targets. However, unexpected events affect the assessment over the longer term. This makes the development of low-cost scientific devices harder than expected. To assure stability and long-term performance of this type of design, continuous evaluation and regular engineering corrections are needed throughout longer testing periods.

Long-term vs. short-term measurements in indoor Rn concentration monitoring: Establishing a procedure for assessing exposure potential (RnEP)

Radon (Rn-222 or Rn) is a naturally occurring chemical element that belongs to the progeny of U-238 released from soil and rocks into the atmosphere, whose impact on organisms is not negligible when it occurs inside confined spaces, such as mine shafts or low-ventilated buildings. The current methodologies for indoor radon assessment are based on passive and active detection. If passive detectors are mainly designed for long-term assessment, active detectors are electronic equipment that can allow on-time visualization of results and, therefore, faster decision-making on risk mitigation measures implementation. However, despite offering a prompt and informed decision, the uncertainty associated with active detector results may cast doubt on the quality of the measurements, which especially arises from the duration of the monitoring campaign. Since radon emanation from soils and rocks depends on a set of variables related to meteorological parameters and soil structure, the seasonal variation of these parameters impacts indoor radon concentration assessment, both on short-term and long-term measurements. Therefore, short-term monitoring campaigns lasting a month, a week, or a day may not produce a statistically representative description of indoor radon exposure for the monitored building. Accordingly, the study compares the reliability of short-term and long-term indoor radon measurements. It proposes a procedure for assessing the indoor Radon Exposure Potential (RnEP) based on in situ measurements through the analysis of the frequency of occurrence of results divided into three classes of potential exposure (low, moderate and high) to reduce occupants' radon risk exposure.

Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment.

Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.

Quantification of influence of monitoring duration on measured traffic action effects on fatigue of RC deck slabs of road bridges

The safety of existing bridges may be assessed using data from monitoring. The fatigue damage induced during observation period is extrapolated to the total service duration of a structure, and the reserve capacity is estimated. However, due to the randomness of road traffic, there exists a risk of underestimation of damage using a short-term monitoring campaign. This paper analyses the influence of monitoring duration on the obtained results using datasets collected during two long-term (>3 years) dynamic monitoring campaigns. Two road bridges with different nature of traffic are considered: (a) a heavily loaded two-lane highway viaduct and (b) a road viaduct with a bi-directional traffic. The deck slabs of both bridges are monitored using strain gauges installed on reinforcement bars. The daily, seasonal, year-to-year and COVID-19 lockdown-induced variations of measured action effects and calculated equivalent fatigue damage are discussed. The resampling method is used for simulation of possible short-term monitoring campaigns during the period considered, with different order of heavy vehicles arriving. The Extreme Value Theory serves to answer what is the required monitoring duration for reliable results. Possible ways of considering the monitoring duration in the fatigue damage calculation are discussed, and the methodology for damage extrapolation proposed.

Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions.

Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO2 and NOx concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

Methodology for assessing the indoor environmental quality in low energy buildings in the Czechia

Contemporary design, realization and operation of buildings based on cost-effective energy savings has an impact on the quality of the indoor environment. We meet completely new questions about the quality of the indoor environment in buildings with low energy consumption in terms of thermal comfort, air quality, acoustic, light, electromagnetic, ionic microclimate and, finally, psychological comfort. The aim of the project is, among other, to introduce methodology for expressing the quality of the indoor environment in low-energy buildings. The reason for this project is the fact that clients are currently receiving detailed information only on the energy performance of the building - energy certificate - but they have virtually no information on real or expected quality of the indoor environment in buildings. The methodology is based on a holistic approach to integrate data from smart building control systems, short-term monitoring campaigns, and questionnaire survey among users. In the case of new buildings, the assessment is based on the simulation of the energy performance of buildings. The environmental quality assessment is based on the principles of EN 15251(revised in EN16798-1). Use of methodology is documented on the results of long-term monitoring of selected IEQ indicators (temperature, CO2, humidity) and questionnaires in nZEB family house.

Capturing the spatial variability of noise levels based on a short-term monitoring campaign and comparing noise surfaces against personal exposures collected through a panel study

Environmental noise can cause important cardiovascular effects, stress and sleep disturbance. The development of appropriate methods to estimate noise exposure within a single urban area remains a challenging task, due to the presence of various transportation noise sources (road, rail, and aircraft). In this study, we developed a land-use regression (LUR) approach using a Generalized Additive Model (GAM) for LAeq (equivalent noise level) to capture the spatial variability of noise levels in Toronto, Canada. Four different model formulations were proposed based on continuous 20-min noise measurements at 92 sites and a leave one out cross-validation (LOOCV). Models where coefficients for variables considered as noise sources were forced to be positive, led to the development of more realistic exposure surfaces. Three different measures were used to assess the models; adjusted R2 (0.44–0.64), deviance (51−72%) and Akaike information criterion (AIC) (469.2–434.6). When comparing exposures derived from the four approaches to personal exposures from a panel study, we observed that all approaches performed very similarly, with values for the Fractional mean bias (FB), normalized mean square error (NMSE), and normalized absolute difference (NAD) very close to 0. Finally, we compared the noise surfaces with data collected from a previous campaign consisting of 1-week measurements at 200 fixed sites in Toronto and observed that the strongest correlations occurred between our predictions and measured noise levels along major roads and highway collectors. Our validation against long-term measurements and panel data demonstrates that manual modifications brought to the models were able to reduce bias in model predictions and achieve a wider range of exposures, comparable with measurement data.

Comparison of Ultrafine Particle and Black Carbon Concentration Predictions from a Mobile and Short-Term Stationary Land-Use Regression Model.

Mobile and short-term monitoring campaigns are increasingly used to develop land-use regression (LUR) models for ultrafine particles (UFP) and black carbon (BC). It is not yet established whether LUR models based on mobile or short-term stationary measurements result in comparable models and concentration predictions. The goal of this paper is to compare LUR models based on stationary (30 min) and mobile UFP and BC measurements from a single campaign. An electric car collected both repeated stationary and mobile measurements in Amsterdam and Rotterdam, The Netherlands. A total of 2964 road segments and 161 stationary sites were sampled over two seasons. Our main comparison was based on predicted concentrations of the mobile and stationary monitoring LUR models at 12 682 residential addresses in Amsterdam. Predictor variables in the mobile and stationary LUR model were comparable, resulting in highly correlated predictions at external residential addresses (R2 of 0.89 for UFP and 0.88 for BC). Mobile model predictions were, on average, 1.41 and 1.91 times higher than stationary model predictions for UFP and BC, respectively. LUR models based upon mobile and stationary monitoring predicted highly correlated UFP and BC concentration surfaces, but predicted concentrations based on mobile measurements were systematically higher.

Space-time UFP and BC models for different geographical areas

Until recently few models existed to assess spatio-temporal variation of ultrafine particles (UFP) and Black carbon (BC). A major complication is the lack of routine monitoring data for these components in many areas. A review of UFP and BC modeling studies was conducted, distinguishing modeling of long-term average spatial contrasts and shorter-term averages (24 hour or less). Dispersion models and geo-statistical models based on monitoring data have been used. We focus on the empirical models here. Most of the identified studies for UFP were based on mobile or short-term monitoring campaigns. For BC models have also been developed based upon more prolonged sampling campaigns. Models based upon mobile monitoring campaigns often explain only a moderate amount of measured spatial variability, typically less than 50%. A problem with UFP models is its specificity, from models of other particle components or gaseous components from similar sources. Models for UFP are now available in a variety of typically urban study areas. Application in epidemiological studies will allow assessment of long-term exposure effects. BC models have been applied extensively in epidemiological studies.

Assessing temporal requirements for SHM campaigns

In the context of limited funds, continuous long-term monitoring may turn out not to be cost-effective to assess structural assets and estimate their service life. Motivated by this, the authors discuss how much data in terms of strain, pavement temperatures and traffic intensities are required to provide estimates of fatigue lives of an orthotropic steel deck. This is done by quantifying the uncertainty reduction due to increasing monitoring training data sets being available. First, several options for monitoring campaigns are defined in terms of the number, the time and the duration of the different monitoring phases associated with each monitoring option. The monitoring outcomes corresponding to each of the different monitoring options are then used to regress the statistical models for fatigue loading prediction based on daily averaged pavement temperatures and daily aggregated heavy-traffic counts. Fatigue reliability profiles are then calculated following a probabilistic methodology to estimate the remaining fatigue life for each location monitored. Finally, the error in determining the fatigue life of a structure detail is calculated for different monitoring options to determine the optimum duration for short-term monitoring campaigns. The proposed approach is illustrated by considering the field data from the Great Belt Bridge (Denmark).

Land Use Regression Models for Ultrafine Particles and Black Carbon Based on Short-Term Monitoring Predict Past Spatial Variation.

Health effects of long-term exposure to ultrafine particles (UFP) have not been investigated in epidemiological studies because of the lack of spatially resolved UFP exposure data. Short-term monitoring campaigns used to develop land use regression (LUR) models for UFP typically had moderate performance. The aim of this study was to develop and evaluate spatial and spatiotemporal LUR models for UFP and Black Carbon (BC), including their ability to predict past spatial contrasts. We measured 30 min at each of 81 sites in Amsterdam and 80 in Rotterdam, The Netherlands in three different seasons. Models were developed using traffic, land use, reference site measurements, routinely measured pollutants and weather data. The percentage explained variation (R(2)) was 0.35-0.40 for BC and 0.33-0.42 for UFP spatial models. Traffic variables were present in every model. The coefficients for the spatial predictors were similar in spatial and spatiotemporal models. The BC LUR model explained 61% of the spatial variation in a previous campaign with longer sampling duration, better than the model R(2). The UFP LUR model explained 36% of UFP spatial variation measured 10 years earlier, similar to the model R(2). Short-term monitoring campaigns may be an efficient tool to develop LUR models.

Spatial variation of ultrafine particles and black carbon in two cities: Results from a short-term measurement campaign

Recently, short-term monitoring campaigns have been carried out to investigate the spatial variation of air pollutants within cities. Typically, such campaigns are based on short-term measurements at relatively large numbers of locations. It is largely unknown how well these studies capture the spatial variation of long term average concentrations. The aim of this study was to evaluate the within-site temporal and between-site spatial variation of the concentration of ultrafine particles (UFPs) and black carbon (BC) in a short-term monitoring campaign.In Amsterdam and Rotterdam (the Netherlands) measurements of number counts of particles larger than 10nm as a surrogate for UFP and BC were performed at 80 sites per city. Each site was measured in three different seasons of 2013 (winter, spring, summer). Sites were selected from busy urban streets, urban background, regional background and near highways, waterways and green areas, to obtain sufficient spatial contrast. Continuous measurements were performed for 30min per site between 9 and 16h to avoid traffic spikes of the rush hour. Concentrations were simultaneously measured at a reference site to correct for temporal variation. We calculated within- and between-site variance components reflecting temporal and spatial variations. Variance ratios were compared with previous campaigns with longer sampling durations per sample (24h to 14days).The within-site variance was 2.17 and 2.44 times higher than the between-site variance for UFP and BC, respectively. In two previous studies based upon longer sampling duration much smaller variance ratios were found (0.31 and 0.09 for UFP and BC). Correction for temporal variation from a reference site was less effective for the short-term monitoring campaign compared to the campaigns with longer duration. Concentrations of BC and UFP were on average 1.6 and 1.5 times higher at urban street compared to urban background sites. No significant differences between the other site types and urban background were found.The high within to between-site concentration variances may result in the loss of precision and low explained variance when average concentrations from short-term campaigns are used to develop land use regression models.

PM<sub>1</sub> measurements at a site close to an oil/gas pre-treatment plant (Agri Valley – southern Italy): a preliminary study

Abstract. A PM1 (i.e. particulate matter with an aerodynamic diameter less than 1.0 μm) short-term monitoring campaign was carried out in the Agri Valley (southern Italy) in September 2012. This area is of international concern, since it houses one of the largest European on-shore reservoirs and the largest oil/gas pre-treatment plant (i.e. the Centro Olio Val d'Agri – COVA) within an anthropised context. PM1 measurements were performed in Viggiano, the nearest town to the COVA plant and one of the most populated towns of the Agri Valley. During the study period, the PM1 daily concentrations ranged from 1.2 to 8.4 μg m−3, with a mean value of 4.6 μg m−3. Regarding the PM1 chemical composition, it can be observed that S and typical crustal elements were the most abundant constituents of the PM1 collected. By applying principal component analysis (PCA), it was pointed out that crustal soil, biomass and wood burning, secondary atmospheric reactions involving COVA plant emissions and local soil particles, and traffic were the main sources contributing to the PM1 measured in the area under study. Moreover, a possible contribution of the long-range transport of African dust was observed.

Spatial distribution of ultrafine particles in urban settings: A land use regression model

The toxic effects of ultrafine particles (UFP) are a public health concern. However, epidemiological studies on the long term effects of UFP are limited due to lacking exposure models. Given the high spatial variation of UFP, the assignment of exposure levels in epidemiological studies requires a fine spatial scale. The aim of this study was to assess the performance of a short-term measurement protocol used at a large number of locations to derive a land use regression (LUR) model of the spatial variation of UFP in Girona, Spain. We measured UFP for 15 min on the sidewalk of 644 participants’ homes in 12 towns of Girona province (Spain). The measurements were done during non-rush traffic hours 9:15–12:45 and 15:15–16:45 during 32 days between June 15 and July 31, 2009. In parallel, we counted the number of vehicles driving in both directions. Measurements were repeated on a different day for a subset of 25 sites in Girona city. Potential predictor variables such as building density, distance to bus lines and land cover were derived using geographic information systems. We adjusted for temporal variation using daily mean NOx concentrations at a central monitor. Land use regression models for the entire area (Core model) and for individual towns were derived using a supervised forward selection algorithm. The best predictors of UFP were traffic intensity, distance to nearest major crossroad, area of high density residential land and household density. The LUR Core model explained 36% of UFP total variation. Adding sampling date and hour of the day to the Core model increased the R2 to 51% without changing the regression slopes. Local models included predictor variables similar to those in the Core model, but performed better with an R2 of 50% in Girona city. Independent LUR models for the first and second measurements at the subset of sites with repetitions had R2’s of about 47%. When the mean of the two measurements was used R2 improved to 72%. LUR models for UFP were developed, based on a highly cost-effective short-term monitoring campaign at a large number of sites, with fair performance. Complementing the approach with further strategies to address sources of temporal variation of UFP is likely to result in improved models as indicated by the good performance of the model based on the subset of sites with one repeated measurement. Our approach is promising for UFP and possibly for other PM components requiring active sampling.

Modelling air pollution for epidemiologic research – Part II: Predicting temporal variation through land use regression

Over recent years land use regression (LUR) has become a frequently used method in air pollution exposure studies, as it can model intra-urban variation in pollutant concentrations at a fine spatial scale. However, very few studies have used the LUR methodology to also model the temporal variation in air pollution exposure. The aim of this study is to estimate annual mean NO 2 and PM 10 concentrations from 1996 to 2008 for Greater Manchester using land use regression models. The results from these models will be used in the Manchester Asthma and Allergy Study (MAAS) birth cohort to determine health effects of air pollution exposure. The Greater Manchester LUR model for 2005 was recalibrated using interpolated and adjusted NO 2 and PM 10 concentrations as dependent variables for 1996-2008. In addition, temporally resolved variables were available for traffic intensity and PM 10 emissions. To validate the resulting LUR models, they were applied to the locations of automatic monitoring stations and the estimated concentrations were compared against measured concentrations. The 2005 LUR models were successfully recalibrated, providing individual models for each year from 1996 to 2008. When applied to the monitoring stations the mean prediction error (MPE) for NO 2 concentrations for all stations and years was -0.8 μg/m³ and the root mean squared error (RMSE) was 6.7 μg/m³. For PM 10 concentrations the MPE was 0.8 μg/m³ and the RMSE was 3.4 μg/m³. These results indicate that it is possible to model temporal variation in air pollution through LUR with relatively small prediction errors. It is likely that most previous LUR studies did not include temporal variation, because they were based on short term monitoring campaigns and did not have historic pollution data. The advantage of this study is that it uses data from an air dispersion model, which provided concentrations for 2005 and 2010, and therefore allowed extrapolation over a longer time period.

Effects of air pollution on Pinus halepensis (Mill.): pollution levels in Attica, Greece

Abstract Air quality in the Athens basin is well known to be poor, damaging building materials and human health, but there is no information available on concentrations or the impact of oxidants on ecosystems in rural areas outside the basin. A short-term monitoring campaign, coupled with a survey using the O3 bioindicator Bel-W3 tobacco demonstrated that phytotoxic concentrations of O3 occurred throughout Attica, within a 75 km radius of the city. There was relatively little injury produced in the industrial centre of the city and maximum injury was found about 40 km to the SE. Visible lesions identical to those of O3 injury were found on Aleppo pine at all sites in the cooler months. In the summer the needles were very chlorotic, possibly due to photo-oxidation of chlorophylls mediated by heat or drought. This chlorosis may mask oxidant symptoms in the summer. NO2 diffusion tube data and needle analysis suggested that the effects of sulphur and NO2 are probably confined to the Athens basin.