Detecting high emitting vehicle subsets using emission remote sensing systems.

It is often assumed that a small proportion of a given vehicle fleet produces a disproportionate amount of air pollution emissions. If true, policy actions to target the highly polluting section of the fleet could lead to significant improvements in air quality. In this paper, high-emitter vehicle subsets are defined and their contributions to the total fleet emission are assessed. A new approach, using enrichment factor in cumulative Pareto analysis is proposed for detecting high emitter vehicle subsets within the vehicle fleet. A large dataset (over 94,000 remote-sensing measurements) from five UK-based EDAR (emission detecting and reporting system) field campaigns for the years 2016-17 is used as the test data. In addition to discussions about the high emitter screening criteria, the data analysis procedure and future issues of implementation are discussed. The results show different high emitter trends dependent on the pollutant investigated, and the vehicle type investigated. For example, the analysis indicates that 23 % and 51 % of petrol and diesel cars were responsible for 80 % of NO emissions within that subset of the fleet, respectively. Overall, the contributions of vehicles that account for 80 % of total fleet emissions usually reduce with EURO class improvement, with the subset fleet emissions becoming more homogenous. The high emitter constituent was more noticeable for pollutant PM compared with the other gaseous pollutants, and it was also more prominent for petrol cars when compared to diesel ones.

Negative-Binomial and quasi-poisson regressions between COVID-19, mobility and environment in São Paulo, Brazil.

Brazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to monitor quarantines and prevent the transmissions of SARS-CoV-2. In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, Brazil. We applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between RMI and COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 4 and 21 days, and 2) the association between COVID-19 and exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility. We found that an RMI of 45.28% results in 1212 cases (95% CI: 1189 to 1235) and 44 deaths (95% CI: 40 to 47). Increasing the isolation from 45.28% to 50% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 µg⋅m-³ of PM2.5 results in a risk of 1.140 (95% CI: 1.021 to 1.274) for cases and 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively. We compared our results with observations and literature review, finding well agreement. Policymakers can use such mobility indices as tools to control social distance activities. Spatial distancing is an important factor to control COVID-19, however, measuring face-mask usage would enhance the understanding the pandemic dynamic. Small increments of air pollution result in an increased number of COVID-19 cases and deaths.

Early observations on the impact of the COVID-19 lockdown on air quality trends across the UK.

UK government implemented national lockdown in response to COVID-19 on the 23-26 March 2020. As elsewhere in Europe and Internationally, associated restrictions initially limited individual mobility and workplace activity to essential services and travel, and significant air quality benefits were widely anticipated. Here, break-point/segment methods are applied to air pollutant time-series from the first half of 2020 to provide an independent estimate of the timings of discrete changes in NO, NO2, NOx, O3, PM10 and PM2.5 time-series from Automatic Urban Rural Network (AURN) monitoring stations across the UK. NO, NO2 and NOx all exhibit abrupt decreases at the time the UK locked down of (on average) 7.6 to 17 µg·m-3 (or 32 to 50%) at Urban Traffic stations and 4 to 5.7 µg·m-3 (or 26 to 46%) at Urban Background stations. However, after the initial abrupt reduction, gradual increases were then observed through lockdown. This suggests that the return of vehicles to the road during early lockdown has already offset much of the air quality improvement seen when locking down (provisional estimate 50 to 70% by 01 July). While locking down O3 increased (7 to 7.4 µg·m-3 or 14 to 17% at Urban stations) broadly in line with NO2 reductions, but later changes suggest significant non-lockdown contributions to O3 during the months that followed. Increases of similar magnitudes were observed for both PM10 (5.9 to 6.3 µg·m-3) and PM2.5 (3.9 to 5.0 µg·m-3) at both Rural and Urban stations alike, but the distribution of changes suggests the lockdown was not an obvious direct source of changes in levels of either of these species during this period, and that more complex contributions, e.g. from resuspension and secondary aerosol, may be more likely major drivers for these changes.

Real-world assessment of vehicle air pollutant emissions subset by vehicle type, fuel and EURO class: New findings from the recent UK EDAR field campaigns, and implications for emissions restricted zones.

This paper reports upon and analyses vehicle emissions measured by the Emissions Detecting and Reporting (EDAR) system, a Vehicle Emissions Remote Sensing System (VERSS) type device, used in five UK based field campaigns in 2016 and 2017. In total 94,940 measurements were made of 75,622 individual vehicles during the five campaigns. The measurements are subset into vehicle type (bus, car, HGV, minibus, motorcycle, other, plant, taxi, van, and unknown), fuel type for car (petrol and diesel), and EURO class, and particulate matter (PM), nitric oxide (NO) and nitrogen dioxide (NO2) are reported. In terms of recent EURO class emission trends, NO and NOx emissions decrease from EURO 5 to EURO 6 for nearly all vehicle categories. Interestingly, taxis show a marked increase in NO2 emissions from EURO 5 to EURO 6. Perhaps most concerningly is a marked increase in PM emissions from EURO 5 to EURO 6 for HGVs. Another noteworthy observation was that vans, buses and HGVs of unknown EURO class were often the dirtiest vehicles in their classes, suggesting that where counts of such vehicles are high, they will likely make a significant contribution to local emissions. Using Vehicle Specific Power (VSP) weighting we provide an indication of the magnitude of the on-site VERSS bias and also a closer estimate of the regulatory test/on-road emissions differences. Finally, a new 'EURO Updating Potential' (EUP) factor is introduced, to assess the effect of a range of air pollutant emissions restricted zones either currently in use or marked for future introduction. In particular, the effects of the London based Low Emission Zone (LEZ) and Ultra-Low Emissions Zone (ULEZ), and the proposed Birmingham based Clean Air Zone (CAZ) are estimated. With the current vehicle fleet, the impacts of the ULEZ and CAZ will be far more significant than the LEZ, which was introduced in 2008.

Generating traffic flow and speed regional model data using internet GPS vehicle records.

Nowadays, many smart-phones and vehicles are equipped with Global Position System (GPS) for tracking and navigation purposes, providing an opportunity to derive highly representative local vehicular flow and estimate vehicular emissions information. Here, we report and discuss methods used to handle large volumes of such activity data, namely 124 million GPS recordings from the web page Maplink.com.br, extract high spatial resolution vehicular flow information for a vast area in South-east Brazil, and correct for bias using traffic counts observations for the same area. The method consists in filter speed and accelerations, assign buffers to the road network, aggregate speed by street, fill missing number of lanes, generate traffic flow. Methods presented here were used to inform traffic-related air quality modelling and used as part of local air pollution management activities but are also amenable to any work that would be enhanced by more locally representative or time-resolved inputs for traffic flow, e.g. traffic network management, and demand modelling. •124 million GPS observations from electronic devices were used to generate traffic flow.•Spatial bias was investigated and accounted for using independent local traffic count data.•Traffic count rescaled GPS traffic flow provide a robust description of spatial and quantitative traffic patterns.

Evaluation of EDAR vehicle emissions remote sensing technology.

Despite much work in recent years, vehicle emissions remain a significant contributor in many areas where air quality standards are under threat. Policy-makers are actively exploring options for next generation vehicle emission control and local fleet management policies, and new monitoring technologies to aid these activities. Therefore, we report here on findings from two separate but complementary blind evaluation studies of one new-to-market real-world monitoring option, HEAT LLC's Emission Detection And Reporting system or EDAR, an above-road open path instrument that uses Differential Absorption LIDAR to provide a highly sensitive and selective measure of passing vehicle emissions. The first study, by Colorado Department of Public Health and Environment and Eastern Research Group, was a simulated exhaust gas test exercise used to investigate the instrumental accuracy of the EDAR. Here, CO, NO, CH4 and C3H8 measurements were found to exhibit high linearity, low bias, and low drift over a wide range of concentrations and vehicle speeds. Instrument accuracy was high (R2 0.996 for CO, 0.998 for NO; 0.983 for CH4; and 0.976 for C3H8) and detection limits were 50 to 100ppm for CO, 10 to 30ppm for NO, 15 to 35ppmC for CH4, and, depending on vehicle speed, 100 to 400ppmC3 for C3H8. The second study, by the Universities of Birmingham and Leeds and King's College London, used the comparison of EDAR, on-board Portable Emissions Measurement System (PEMS) and car chaser (SNIFFER) system measurements collected under real-world conditions to investigate in situ EDAR performance. Given the analytical challenges associated with aligning these very different measurements, the observed agreements (e.g. EDAR versus PEMS R2 0.92 for CO/CO2; 0.97 for NO/CO2; ca. 0.82 for NO2/CO2; and, 0.94 for PM/CO2) were all highly encouraging and indicate that EDAR also provides a representative measure of vehicle emissions under real-world conditions.

Quantifying temporal trends in ground level ozone concentration in the UK.

There is a high interest in quantifying temporal trends in surface ozone concentrations as they serve to quantify the impacts of the anthropogenic precursor reductions and to assess the effects of emission control strategies. In this paper ozone trends for nearly 2 decades (1993 to 2011) at both rural and urban sites have been analysed, using ground level ozone data from 5 urban and 15 rural sites, which are part of the UK AURN. This study analyses ozone trends at various percentiles, in addition to traditional mean trends using quantile regression, TheilSen function, and changepoint analysis. Ozone trends show significant variability at different statistical metrics (e.g., mean, median, maximum and selected quantiles). Maximum trends were negative, whereas median and mean trends were positive during the study period (1993-2011) at both rural and urban sites. Urban and rural trends show different rates of change and indicate that urban decrement (the difference in ozone concentration between rural and urban areas) has been decreasing over the period. Ozone trends were negative during the last 8 years (2004-2011), which could have been caused by the stabilisation of NOx concentration during this period. Furthermore, 3 changepoints were detected in the temporal trend using Pruned Exact Linear Time (PELT) search algorithm, which provides further insight into the ozone temporal trends.

Methodology for fitting and updating predictive accident models with trend.

Reliable predictive accident models (PAMs) (also referred to as Safety Performance Functions (SPFs)) have a variety of important uses in traffic safety research and practice. They are used to help identify sites in need of remedial treatment, in the design of transport schemes to assess safety implications, and to estimate the effectiveness of remedial treatments. The PAMs currently in use in the UK are now quite old; the data used in their development was gathered up to 30 years ago. Many changes have occurred over that period in road and vehicle design, in road safety campaigns and legislation, and the national accident rate has fallen substantially. It seems unlikely that these ageing models can be relied upon to provide accurate and reliable predictions of accident frequencies on the roads today. This paper addresses a number of methodological issues that arise in seeking practical and efficient ways to update PAMs, whether by re-calibration or by re-fitting. Models for accidents on rural single carriageway roads have been chosen to illustrate these issues, including the choice of distributional assumption for overdispersion, the choice of goodness of fit measures, questions of independence between observations in different years, and between links on the same scheme, the estimation of trends in the models, the uncertainty of predictions, as well as considerations about the most efficient and convenient ways to fit the required models.

Near-field commercial aircraft contribution to nitrogen oxides by engine, aircraft type, and airline by individual plume sampling.

Nitrogen oxides (NOx) concentrations were measured in individual plumes from aircraft departing on the northern runway at Heathrow Airport in west London. Over a period of four weeks 5618 individual plumes were sampled by a chemiluminescence monitor located 180 m from the runway. Results were processed and matched with detailed aircraft movement and aircraft engine data using chromatographic techniques. Peak concentrations associated with 29 commonly used engines were calculated and found to have a good relationship with N0x emissions taken from the International Civil Aviation Organization (ICAO) databank. However, it is found that engines with higher reported NOx emissions result in proportionately lower NOx concentrations than engines with lower emissions. We show that it is likely that aircraft operational factors such as takeoff weight and aircraftthrust setting have a measurable and important effect on concentrations of N0x. For example, NOx concentrations can differ by up to 41% for aircraft using the same airframe and engine type, while those due to the same engine type in different airframes can differ by 28%. These differences are as great as, if not greater than, the reported differences in NOx emissions between different engine manufacturers for engines used on the same airframe.

Real-world comparison of probe vehicle emissions and fuel consumption using diesel and 5% biodiesel (B5) blend.

An instrumented EURO I Ford Mondeo was used to perform a real-world comparison of vehicle exhaust (carbon dioxide, carbon monoxide, hydrocarbons and oxides of nitrogen) emissions and fuel consumption for diesel and 5% biodiesel in diesel blend (B5) fuels. Data were collected on multiple replicates of three standardised on-road journeys: (1) a simple urban route; (2) a combined urban/inter-urban route; and, (3) an urban route subject to significant traffic management. At the total journey measurement level, data collected here indicate that replacing diesel with a B5 substitute could result in significant increases in both NO(x) emissions (8-13%) and fuel consumption (7-8%). However, statistical analysis of probe vehicle data demonstrated the limitations of comparisons based on such total journey measurements, i.e., methods analogous to those used in conventional dynamometer/drive cycle fuel comparison studies. Here, methods based on the comparison of speed/acceleration emissions and fuel consumption maps are presented. Significant variations across the speed/acceleration surface indicated that direct emission and fuel consumption impacts were highly dependent on the journey/drive cycle employed. The emission and fuel consumption maps were used both as descriptive tools to characterise impacts and predictive tools to estimate journey-specific emission and fuel consumption effects.

Change-point detection of gaseous and particulate traffic-related pollutants at a roadside location.

An 8-year (1998-2005), hourly data set of measurements of NOx, NO2, PM10, PM2.5, and PMcoarse (defined as PM(2.5-10)) from a busy roadside location in central London has been analyzed to identify important change-points in the time series using a cumulative sum (CUSUM) technique. Randomization methods were used to estimate the uncertainty level associated with the change-points with uncertainty intervals derived using a bootstrap approach. The results show that there is a clear change-point increase for NO2 coinciding with the introduction of the London congestion-charging in February 2003 (95% confidence interval from January-March 2003). At this time there was both an increase in bus numbers and buses fitted with catalyzed diesel particulate filters, which increase direct emissions of NO2. A highly statistically significant change-point was also observed for PMcoarse (95% confidence interval from December 2002-February 2003), which also occurred close to the time of the congestion charge introduction and is most closely related to the increase in bus flows. The increase in PMcoarse at this time has largely compensated for reductions in the concentration of PM2.5, such that the concentration of PM10 has remained almost constant. Comparing the 2 years before and after the introduction of congestion charging, the increment in NO2 above background increased from 22 to 34 ppb and PMcoarse increased from 4 to 9 microg m(-3). These results could have important implications for meeting European air quality standards that currently set limits for PMlo rather than PM2.5.

Development of hazard analysis by critical control points (HACCP) procedures to control organic chemical hazards in the agricultural production of raw food commodities.

Hazard Analysis by Critical Control Points (HACCP) is a systematic approach to the identification, assessment, and control of hazards in the food chain. Effective HACCP requires the consideration of all chemical microbiological, and physical hazards. However, current procedures focus primarily on microbiological and physical hazards, while chemical aspects of HACCP have received relatively little attention. In this article we discuss the application of HACCP to organic chemical contaminants and the problems that are likely to be encountered in agriculture. We also present generic templates for the development of organic chemical contaminant HACCP procedures for selected raw food commodities, that is, cereal crops,raw meats, and milk.