Development of Data Analytic Approaches for Air Quality Data

Continuous air quality monitoring networks were commissioned in the mid-twentieth century throughout the developed world to underpin the understanding of air pollution. These monitoring networks have produced a vast observational record which continues to grow. However, these data are generally used for simple tasks such as checking for compliance to legal standards or guidelines and the additional information contained in the data sets is not well leveraged to aid scientific understanding and inform policy makers. This thesis addresses this issue and has the goal of extracting additional information from "routine"' air quality monitoring data using new, and novel data analyses with a focus on the impact of transportation activities across Europe. Specifically, this thesis outlines the development of bivariate polar plots with pair-wise statistics to aid source apportionment, the development of a European air quality database which much of this thesis's work is based on, a European-wide analysis of roadside nitrogen dioxide (NO2), and the development of a framework and software to robustly detect and quantify changes in pollutant concentrations. The additional functionality of bivariate polar plots was useful for isolating the natural and anthropogenic sources of pollutants and is now included in the open source openair R package. The NO2 analysis revealed that directly emitted NO2 from road vehicles is decreasing across Europe and assumed emissions are too high resulting in pessimistic projections of future compliance. This conclusion is very important for policy makers to consider in their planning of disruptive interventions, most relevant of which are low emission zones because the observations suggest that the outlook is better than traditionally thought. For those analysing trends, a new technique has been developed that is highly effective at robustly characterising and quantifying the effects of interventions and the tools developed are available in the form of the open source rmweather R package

Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application

Low cost sensors for measuring atmospheric pollutants are experiencing an increase in popularity worldwide among practitioners, academia and environmental agencies, and a large amount of data by these devices are being delivered to the public. Notwithstanding their behaviour, performance and reliability are not yet fully investigated and understood. In the present study we investigate the medium term performance of a set of NO and NO2 electrochemical sensors in Switzerland using three different regression algorithms within a field calibration approach. In order to mimic a realistic application of these devices, the sensors were initially co-located at a rural regulatory monitoring site for a 4-month calibration period, and subsequently deployed for 4 months at two distant regulatory urban sites in traffic and urban background conditions, where the performance of the calibration algorithms was explored. The applied algorithms were Multivariate Linear Regression, Support Vector Regression and Random Forest; these were tested, along with the sensors, in terms of generalisability, selectivity, drift, uncertainty, bias, noise and suitability for spatial mapping intra-urban pollution gradients with hourly resolution. Results from the deployment at the urban sites show a better performance of the non-linear algorithms (Support Vector Regression and Random Forest) achieving RMSE R2 between 0.74 and 0.95 and MAE between 2 and 4\u11d\u20acppb. The combined use of both NO and NO2 sensor output in the estimate of each pollutant showed some contribution by NO sensor to NO2 estimate and vice-versa. All algorithms exhibited a drift ranging between 5 and 10\u11d\u20acppb for Random Forest and 15\u11d\u20acppb for Multivariate Linear Regression at the end of the deployment. The lowest concentration correctly estimated, with a 25\u11d\u20ac% relative expanded uncertainty, resulted in ca. 15\u201320\u11d\u20acppb and was provided by the non-linear algorithms. As an assessment for the suitability of the tested sensors for a targeted application, the probability of resolving hourly concentration difference in cities was investigated. It was found that NO concentration differences of 5\u201310\u11d\u20acppb (8\u201310 for NO2) can reliably be detected (90\u11d\u20ac% confidence), depending on the air pollution level. The findings of this study, although derived from a specific sensor type and sensor model, are based on a flexible methodology and have extensive potential for exploring the performance of other low cost sensors, that are different in their target pollutant and sensing technology

Random forest meteorological normalisation models for Swiss PM10 trend analysis

Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trend analysis because there is more certainty that the observed trends are due to changes in emissions or chemistry, not changes in meteorology. Predictive random forest models (RF; a decision tree machine learning technique) were grown for 31 air quality monitoring sites in Switzerland using surface meteorological, synoptic scale, boundary layer height, and time variables to explain daily PM10 concentrations. The RF models were used to calculate meteorologically normalised trends which were formally tested and evaluated using the Theil–Sen estimator. Between 1997 and 2016, significantly decreasing normalised PM10 trends ranged between −0.09 and −1.16 μg m−3 year−1 with urban traffic sites experiencing the greatest mean decrease in PM10 concentrations at −0.77 μg m−3 year−1. Similar magnitudes have been reported for normalised PM10 trends for earlier time periods in Switzerland which indicates PM10 concentrations are continuing to decrease at similar rates as in the past. The ability for RF models to be interpreted was leveraged using partial dependence plots to explain the observed trends and relevant physical and chemical processes influencing PM10 concentrations. Notably, two regimes were suggested by the models which cause elevated PM10 concentrations in Switzerland: one related to poor dispersion conditions and a second resulting from high rates of secondary PM generation in deep, photochemically active boundary layers. The RF meteorological normalisation process was found to be robust, user friendly and simple to implement, and readily interpretable which suggests the technique could be useful in many air quality exploratory data analysis situations

Post-Dieselgate : Evidence of NOx Emission Reductions Using On-Road Remote Sensing

The Dieselgate scandal which broke in September 2015 demonstrated that vehicle manufacturers, such as the Volkswagen Group (VWG), engaged in software-based manipulation which led to vehicles passing laboratory-based emission testing limits but were far more polluting while being driven on roads. Using 23 000 on-road remote sensing measurements of light-duty Euro 5 diesel vehicles in the United Kingdom between 2012 and 2018, VWG vehicles with the "Dieselgate-affected" EA189 engine demonstrated anomalous NOx emission behavior between the pre- and post-Dieselgate periods which was not observed in other vehicle makes or models. These anomalous changes can be explained by voluntary VWG hardware and software fixes which have led to improved NOx emission control. The VGW 1.6 L vehicles, with a simple hardware fix and a software upgrade, resulted in a 36% reduction in NOx, whereas the 2.0 L vehicles that required a software-only fix showed a 30% reduction in NOx once controlled for ambient temperature effects. These results show that even minor changes or upgrades can considerably reduce NOx emissions, which has implications for future emission control activities and local air quality

Variability in remission in family therapy for anorexia nervosa.

ObjectiveThe evolution toward more stringent conceptualizations of remission in family therapy for adolescent anorexia nervosa (AN) has, with time, introduced variability in outcomes across randomized controlled trials (RCTs). An examination of remission across the history of research on family therapy for AN shows that earlier studies adopted lenient definitions and generally yielded higher rates of remission than studies of the past decade that have used stricter definitions of remission. In this study, we investigate the reactivity of remission rates to the application of different definitions of remission used within the family therapy for AN literature, within a single RCT data set.MethodWe conducted a secondary analysis of data from a single-site RCT which compared the relative efficacy of two formats of family therapy in a sample of 106 Australian adolescents with AN. Using end-of-treatment data, we compared remission rates using 11 definitions of remission that have been used in studies of family therapy for AN spanning more than three decades.ResultsWe found wide variability in remission rates (21.7-87.7%; Cochran's Q χ2 (10, N = 106) = 303.55, p = .000], depending on which definition of remission was applied. As expected, more lenient criteria produced higher remission rates than more stringent definitions.DiscussionApplying different criteria of remission to a single data set illustrates the impact of changing how remission is defined. Failure to consider the greater stringency of remission criteria in recent studies could result in false inferences concerning the efficacy of family therapy for AN over time

Electron Bio-Imaging Centre (eBIC): the UK national research facility for biological electron microscopy

The recent resolution revolution in cryo-EM has led to a massive increase in demand for both time on high-end cryo-electron microscopes and access to cryo-electron microscopy expertise. In anticipation of this demand, eBIC was set up at Diamond Light Source in collaboration with Birkbeck College London and the University of Oxford, and funded by the Wellcome Trust, the UK Medical Research Council (MRC) and the Biotechnology and Biological Sciences Research Council (BBSRC) to provide access to high-end equipment through peer review. eBIC is currently in its start-up phase and began by offering time on a single FEI Titan Krios microscope equipped with the latest generation of direct electron detectors from two manufacturers. Here, the current status and modes of access for potential users of eBIC are outlined. In the first year of operation, 222 d of microscope time were delivered to external research groups, with 95 visits in total, of which 53 were from unique groups. The data collected have generated multiple high- to intermediate-resolution structures (2.8–8 Å), ten of which have been published. A second Krios microscope is now in operation, with two more due to come online in 2017. In the next phase of growth of eBIC, in addition to more microscope time, new data-collection strategies and sample-preparation techniques will be made available to external user groups. Finally, all raw data are archived, and a metadata catalogue and automated pipelines for data analysis are being developed

Strong Temperature Dependence for Light-Duty Diesel Vehicle NOx Emissions

Diesel-powered road vehicles are important sources for nitrogen oxide (NOx) emissions, and the European passenger fleet is highly dieselised, which has resulted in many European roadside environments being noncompliant with legal air quality standards for nitrogen dioxide (NO2). On the basis of vehicle emission remote sensing data for 300000 light-duty vehicles across the United Kingdom, light-duty diesel NOx emissions were found to be highly dependent on ambient temperature with low temperatures resulting in higher NOx emissions, i.e., a "low temperature NOx emission penalty" was identified. This feature was not observed for gasoline-powered vehicles. Older Euro 3 to 5 diesel vehicles emitted NOx similarly, but vehicles compliant with the latest Euro 6 emission standard emitted less NOx than older vehicles and demonstrated less of an ambient temperature dependence. This ambient temperature dependence is overlooked in current emission inventories but is of importance from an air quality perspective. Owing to Europe's climate, a predicted average of 38% more NOx emissions have burdened Europe when compared to temperatures encountered in laboratory test cycles. However, owing to the progressive elimination of vehicles demonstrating the most severe low temperature NOx penalty, light-duty diesel NOx emissions are likely to decrease more rapidly throughout Europe than currently thought

A case study application of machine-learning for the detection of greenhouse gas emission sources

Conclusively linking local, episodic enhancements in greenhouse gas concentrations to a specific emission source can be challenging, particularly when faced with multiple proximal sources of emissions and variable meteorology, and in the absence of co-emitted tracer gases. This study demonstrates and evaluates the efficacy of using machine-learning tools to detect episodic emissions of methane (CH4) from a shale gas extraction facility in Lancashire (United Kingdom). Two machine-learning tools (rmweather and Prophet) were trained using a two-year climatological baseline dataset collected prior to gas extraction operations at the facility. The baseline dataset consisted of high-precision trace gas concentrations and meteorological data, sampled at 1 Hz continuously between 2016 and 2019. The models showed good overall predictive capacity for baseline CH4 concentrations, with R2 values of 0.85 and 0.76 under optimised training conditions for rmweather and Prophet, respectively. CH4 concentrations were then forecast for an 18-month period from the onset of operations at the shale gas facility (in 2018). Forecast values were compared with true measurements to detect anomalous deviations that may indicate the presence of new emission events associated with the operational facility. Both models successfully detected two periods in which CH4 emissions were known to have occurred (December 2018 and January 2019) via anomalous deviations between modelled and measured concentrations. This work demonstrates the application of machine-learning models for the detection of CH4 emission events from newly built industrial sources, when used in combination with real-time atmospheric monitoring and a baseline dataset collected prior to installation

The impact of plug-in fragrance diffusers on residential indoor VOC concentrations

Plug-in fragrance diffusers are one of myriad volatile organic compound-containing consumer products that are commonly found in homes. The perturbing effects of using a commercial diffuser indoors were evaluated using a study group of 60 homes in Ashford, UK. Air samples were taken over 3 day periods with the diffuser switched on and in a parallel set of control homes where it was off. At least four measurements were taken in each home using vacuum-release into 6 L silica-coated canisters and with >40 VOCs quantified using gas chromatography with FID and MS (GC-FID-QMS). Occupants self-reported their use of other VOC-containing products. The variability between homes was very high with the 72 hour sum of all measured VOCs ranging between 30 and >5000 μg m−3, dominated by n/i-butane, propane, and ethanol. For those homes in the lowest quartile of air exchange rate (identified using CO2 and TVOC sensors as proxies) the use of a diffuser led to a statistically significant increase (p-value < 0.02) in the summed concentration of detectable fragrance VOCs and some individual species, e.g. alpha pinene rising from a median of 9 μg m−3 to 15 μg m−3 (p-value < 0.02). The observed increments were broadly in line with model-calculated estimates based on fragrance weight loss, room sizes and air exchange rates