Meteorologically normalised long-term trends of atmospheric ammonia (NH3) in Switzerland/Liechtenstein and the explanatory role of gas-aerosol partitioning.

Ammonia (NH3) is an important atmospheric pollutant and despite significant management efforts, trends of NH3 concentrations have not shown progressive decreases over the last few decades across much of Europe. To investigate this issue, long-term NH3 concentrations from passive sampling tubes were analysed at 32 locations across Switzerland and Liechtenstein. A trend analysis controlling for changes in meteorology employing generalised additive models (GAMs) between 2000 and 2021 showed that 29 of the 32 (91 %) sites experienced no significant change or increasing NH3 concentrations with the greatest trend being 0.17 µgm-3y-1. These results conflict with an indicated 13 % reduction in NH3 emissions from the Swiss emission inventory. The sensitivity of the NH3 -ammonium (NH4+) system to reductions of NH3 's acidic sinks (mostly in the form of nitric and sulfuric acids) was investigated with thermodynamic equilibrium modelling to explain this disconnect. The simulations indicated that the reductions in NH3 's acidic sinks resulted in less NH4+ transformation, thus increasing the NH3/NHx ratio and this process has compensated for the reduction in NH3 emissions. The average effect of the sink reductions was an increase of 0.9 µgm-3 in NH3 between 2004 and 2021. Increases in the NH3/NHx ratio have likely occurred in many European countries due to reductions of acidic precursor emissions and will have consequences for reactive nitrogen deposition and alter import-export budgets among neighbouring regions and countries.

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.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia.

In this paper, the authors investigated changes in mass concentrations of particulate matter (PM) during the Coronavirus Disease of 2019 (COVID-19) lockdown. Daily samples of PM1, PM2.5 and PM10 fractions were measured at an urban background sampling site in Zagreb, Croatia from 2009 to late 2020. For the purpose of meteorological normalization, the mass concentrations were fed alongside meteorological and temporal data to Random Forest (RF) and LightGBM (LGB) models tuned by Bayesian optimization. The models' predictions were subsequently de-weathered by meteorological normalization using repeated random resampling of all predictive variables except the trend variable. Three pollution periods in 2020 were examined in detail: January and February, as pre-lockdown, the month of April as the lockdown period, as well as June and July as the "new normal". An evaluation using normalized mass concentrations of particulate matter and Analysis of variance (ANOVA) was conducted. The results showed that no significant differences were observed for PM1, PM2.5 and PM10 in April 2020-compared to the same period in 2018 and 2019. No significant changes were observed for the "new normal" as well. The results thus indicate that a reduction in mobility during COVID-19 lockdown in Zagreb, Croatia, did not significantly affect particulate matter concentration in the long-term..

Frequency of use of household products containing VOCs and indoor atmospheric concentrations in homes.

Volatile organic compounds (VOCs) are a key class of atmospheric emission released from highly complex petrochemical, transport and solvent sources both outdoors and indoors. This study established the concentrations and speciation of VOCs in 60 homes (204 individuals, 360 × 72 h samples, 40 species) in summer and winter, along with outdoor controls. Self-reported daily statistics were collected in each home on the use of cleaning, household and personal care products, all of which are known to release VOCs. Frequency of product use varied widely: deodorants: 2.9 uses home per day; sealant-mastics 0.02 uses home per day. The total concentration of VOCs indoors (range C2-C10) was highly variable between homes e.g. range 16.6-8150 µg m-3 in winter. Indoor concentrations of VOCs exceeded outdoor for 84% of households studied in summer and 100% of homes in winter. The most abundant VOCs found indoors in this study were n-butane (wintertime range: 1.5-4630 µg m-3), likely released as aerosol propellant, ethanol, acetone and propane. The cumulative use VOC-containing products over multiday timescales by occupants provided little predictive power to infer 72 hour averaged indoor concentrations. However, there was weak covariance between the cumulative usage of certain products and individual VOCs. From a domestic emissions perspective, reducing the use of hydrocarbon-based aerosol propellants indoors would likely have the largest impact.

Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning.

During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10 (particulate matter), O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city's lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. The analysis indicated that the city's average concentration reductions for the lockdown period were: -36.9 to -41.6%, and -6.6 to -14.2% for NO2 and PM10, respectively. However, an increase of 11.6-33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (-51.6 to -43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.

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.

Strong Temperature Dependence for Light-Duty Diesel Vehicle NO x Emissions.

Diesel-powered road vehicles are important sources for nitrogen oxide (NO x) 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 NO x emissions were found to be highly dependent on ambient temperature with low temperatures resulting in higher NO x emissions, i.e., a "low temperature NO x emission penalty" was identified. This feature was not observed for gasoline-powered vehicles. Older Euro 3 to 5 diesel vehicles emitted NO x similarly, but vehicles compliant with the latest Euro 6 emission standard emitted less NO x 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 NO x 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 NO x penalty, light-duty diesel NO x emissions are likely to decrease more rapidly throughout Europe than currently thought.

Using meteorological normalisation to detect interventions in air quality time series.

Interventions used to improve air quality are often difficult to detect in air quality time series due to the complex nature of the atmosphere. Meteorological normalisation is a technique which controls for meteorology/weather over time in an air quality time series so intervention exploration (and trend analysis) can be assessed in a robust way. A meteorological normalisation technique, based on the random forest machine learning algorithm was applied to routinely collected observations from two locations where known interventions were imposed on transportation activities which were expected to change ambient pollutant concentrations. The application of progressively stringent limits on the content of sulfur in marine fuels was very clearly represented in ambient sulfur dioxide (SO2) monitoring data in Dover, a port city in the South East of England. When the technique was applied to the oxides of nitrogen (NOx and NO2) time series at London Marylebone Road (a Central London monitoring site located in a complex urban environment), the normalised time series highlighted clear changes in NO2 and NOx which were linked to changes in primary (directly emitted) NO2 emissions at the location. The clear features in the time series were illuminated by the meteorological normalisation procedure and were not observable in the raw concentration data alone. The lack of a need for specialised inputs, and the efficient handling of collinearity and interaction effects makes the technique flexible and suitable for a range of potential applications for air quality intervention exploration.

High density ozone monitoring using gas sensitive semi-conductor sensors in the Lower Fraser Valley, British Columbia.

A cost-efficient technology for accurate surface ozone monitoring using gas-sensitive semiconducting oxide (GSS) technology, solar power, and automated cell-phone communications was deployed and validated in a 50 sensor test-bed in the Lower Fraser Valley of British Columbia, over 3 months from May-September 2012. Before field deployment, the entire set of instruments was colocated with reference instruments for at least 48 h, comparing hourly averaged data. The standard error of estimate over a typical range 0-50 ppb for the set was 3 ± 2 ppb. Long-term accuracy was assessed over several months by colocation of a subset of ten instruments each at a different reference site. The differences (GSS-reference) of hourly average ozone concentration were normally distributed with mean -1 ppb and standard deviation 6 ppb (6000 measurement pairs). Instrument failures in the field were detected using network correlations and consistency checks on the raw sensor resistance data. Comparisons with modeled spatial O3 fields demonstrate the enhanced monitoring capability of a network that was a hybrid of low-cost and reference instruments, in which GSS sensors are used both to increase station density within a network as well as to extend monitoring into remote areas. This ambitious deployment exposed a number of challenges and lessons, including the logistical effort required to deploy and maintain sites over a summer period, and deficiencies in cell phone communications and battery life. Instrument failures at remote sites suggested that redundancy should be built into the network (especially at critical sites) as well as the possible addition of a "sleep-mode" for GSS monitors. At the network design phase, a more objective approach to optimize interstation distances, and the "information" content of the network is recommended. This study has demonstrated the utility and affordability of the GSS technology for a variety of applications, and the effectiveness of this technology as a means substantially and economically to extend the coverage of an air quality monitoring network. Low-cost, neighborhood-scale networks that produce reliable data can be envisaged.