Reconstructing urban CO₂ emissions utilising the radiocarbon composition of tree rings from the Wellington Region, New Zealand

<p>This study demonstrates the utility of tree ring radiocarbon analysis to quantify a temporal record of recently-added fossil fuel-derived carbon dioxide (CO₂ff) in the urban atmosphere, to retrospectively measure emissions and potentially validate local emissions inventories. Currently, there is no internationally recognised method to test emissions inventories against direct atmospheric estimations of CO₂ff. With the increasing interest in emissions control legislation, independent and objective research to validate emissions reported by governments and industries is needed. As CO₂ff emissions are completely depleted in radiocarbon (¹⁴C), an observed decrease in the ¹⁴C content of the atmosphere is mostly due to additions of CO₂ff. As trees incorporate CO₂ from the local atmosphere into annual growth rings, it was hypothesised that an urban located tree would reflect emission rates of its local surroundings. Measurements of the ¹⁴C content of cellulose were made from the annual tree rings of a Kauri tree (Agathis australis), located in the downtown area of the Wellington suburb of Lower Hutt (KNG52). This record was compared with tree rings from two Kauri at a nearby coastal site (NIK19 and NIK23) and the long-term clean air ¹⁴CO₂ record from Baring Head. The clean air Kauri trees, NIK19 and NIK23, demonstrated excellent agreement with the Baring Head atmospheric record, indicating that the trees were accurately sampling the atmosphere. The KNG52 tree, demonstrated good agreement with the clean air record in the early part of the record (with some variability), however, exhibited significantly lower Δ¹⁴CO₂ values from the 1980s onward. Calculation of the influence of the terrestrial biosphere on the ¹⁴CO₂ record showed very little impact, determining that the variability seen was due to local additions of CO₂ff. Historic CO₂ff emissions were calculated using the Δ¹⁴CO₂ measurements from the KNG52 ¹⁴CO₂ record for the period 1972 – 2012. Biosphere correction calculations showed that the biosphere was the dominant influence on the record in the early part of the record (1972 – 1980), with fossil fuel emissions dominating the record from 1980s onward. The observations were compared qualitatively with meteorological data and urban development in the area to assess variability in CO₂ff. A minor trend towards lower wind speeds associated with higher levels of CO₂ff was identified, indicating that local meteorology may be responsible for 10% change seen in the record. The influence of local development demonstrated some possible relation but a correlation was not significant. The KNG52 CO₂ff record was compared with national-level reported liquid (road traffic) emissions from the Carbon Dioxide Information Analysis Centre (CDIAC). The observed KNG52 CO₂ff in the tree ring record appeared to increase in tandem with road traffic emissions.</p>

Reconstructing urban CO₂ emissions utilising the radiocarbon composition of tree rings from the Wellington Region, New Zealand

<p>This study demonstrates the utility of tree ring radiocarbon analysis to quantify a temporal record of recently-added fossil fuel-derived carbon dioxide (CO₂ff) in the urban atmosphere, to retrospectively measure emissions and potentially validate local emissions inventories. Currently, there is no internationally recognised method to test emissions inventories against direct atmospheric estimations of CO₂ff. With the increasing interest in emissions control legislation, independent and objective research to validate emissions reported by governments and industries is needed. As CO₂ff emissions are completely depleted in radiocarbon (¹⁴C), an observed decrease in the ¹⁴C content of the atmosphere is mostly due to additions of CO₂ff. As trees incorporate CO₂ from the local atmosphere into annual growth rings, it was hypothesised that an urban located tree would reflect emission rates of its local surroundings. Measurements of the ¹⁴C content of cellulose were made from the annual tree rings of a Kauri tree (Agathis australis), located in the downtown area of the Wellington suburb of Lower Hutt (KNG52). This record was compared with tree rings from two Kauri at a nearby coastal site (NIK19 and NIK23) and the long-term clean air ¹⁴CO₂ record from Baring Head. The clean air Kauri trees, NIK19 and NIK23, demonstrated excellent agreement with the Baring Head atmospheric record, indicating that the trees were accurately sampling the atmosphere. The KNG52 tree, demonstrated good agreement with the clean air record in the early part of the record (with some variability), however, exhibited significantly lower Δ¹⁴CO₂ values from the 1980s onward. Calculation of the influence of the terrestrial biosphere on the ¹⁴CO₂ record showed very little impact, determining that the variability seen was due to local additions of CO₂ff. Historic CO₂ff emissions were calculated using the Δ¹⁴CO₂ measurements from the KNG52 ¹⁴CO₂ record for the period 1972 – 2012. Biosphere correction calculations showed that the biosphere was the dominant influence on the record in the early part of the record (1972 – 1980), with fossil fuel emissions dominating the record from 1980s onward. The observations were compared qualitatively with meteorological data and urban development in the area to assess variability in CO₂ff. A minor trend towards lower wind speeds associated with higher levels of CO₂ff was identified, indicating that local meteorology may be responsible for 10% change seen in the record. The influence of local development demonstrated some possible relation but a correlation was not significant. The KNG52 CO₂ff record was compared with national-level reported liquid (road traffic) emissions from the Carbon Dioxide Information Analysis Centre (CDIAC). The observed KNG52 CO₂ff in the tree ring record appeared to increase in tandem with road traffic emissions.</p>

A Study of Traffic Emissions Based on Floating Car Data for Urban Scale Air Quality Applications

Urban air quality in cities is strongly influenced by road traffic emissions. Micro-scale models have often been used to evaluate the pollutant concentrations at the scale of the order of meters for estimating citizen exposure. Nonetheless, retrieving emissions information with the required spatial and temporal details is still not an easy task. In this work, we use our modelling system PMSS (Parallel Micro Swift Spray) with an emission dataset based on Floating Car Data (FCD), containing hourly data for a large number of road links within a 1 × 1 km2 domain in the city of Rome for the month of May 2013. The procedures to obtain both the emission database and the PMSS simulations are hosted on CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID HPC facilities managed by ENEA. The possibility of using such detailed emissions, coupled with HPC performance, represents a desirable goal for microscale modeling that can allow such modeling systems to be employed in quasi-real time and nowcasting applications. We compute NOx concentrations obtained by: (i) emissions coming from prescribed hourly modulations of three types of roads, based on vehicle flux data in the FCD dataset, and (ii) emissions from the FCD dataset integrated into our modelling chain. The results of the simulations are then compared to concentrations measured at an urban traffic station.

TransClim (v1.0): A chemistry-climate response model for assessing the effect of mitigation strategies for road traffic on ozone

Road traffic emits not only carbon dioxide (CO2), but also other pollutants such as nitrogen oxides (NOx), volatile organic compounds (VOC) and carbon monoxide (CO). These chemical species influence the atmospheric chemistry and produce ozone (O3) in the troposphere. Ozone acts as a greenhouse gas and thus contributes to anthropogenic global warming. Technological trends and political decisions can help to reduce the O3 effect of road traffic emissions on climate. In order to assess the O3 response of such mitigation options on climate, we developed a chemistry-climate response model called TransClim (Modelling the effect of surface Transportation on Climate). It considers road traffic emissions of NOx, VOC and CO and determines the O3 change and its corresponding stratospheric-adjusted radiative forcing. Using a tagging method, TransClim is further able to quantify the contribution of road traffic emissions to the O3 concentration. The response model bases on lookup-tables which are generated by a set of emission variation simulations performed with the global chemistry climate model EMAC (ECHAM5 v5.3.02, MESSy v2.53.0). Evaluating TransClim against independent EMAC simulations reveals very low deviations of all considered species (0.01–7 %). Hence, TransClim is able to reproduce the results of an EMAC simulation very well. Moreover, TransClim is about 6000 times faster in computing the climate effect of an emission scenario than the complex chemistry-climate model. This makes TransClim a suitable tool to efficiently assess the climate effect of a broad range of mitigation options for road traffic or to analyse uncertainty ranges by employing Monte-Carlo simulations.

Quantifying Air Pollutant Emission from Agricultural Machinery Using Surveys—A Case Study in Anhui, China

Diesel-powered agricultural machinery (AM) is a significant contributor to air pollutant emissions, including nitrogen oxides (NOx) and particulate matter (PM). However, the fuel consumption and pollutant emissions from AM remain poorly quantified in many countries due to a lack of accurate activity data and emissions factors. In this study, the fuel consumption and air pollutant emission from AM were estimated using a survey and emission factors from the literature. A case study was conducted using data collected in Anhui, one of the agricultural provinces of China. The annual active hours of AM in Anhui ranged 130 to 175 h. The estimated diesel fuel consumption by AM was 1.45 Tg in 2013, approximately 25% of the total diesel consumption in the province. The air pollutants emitted by AM were 57 Gg of carbon monoxide, 14 Gg of hydrocarbon, 74 Gg of NOx and 5.7 Gg of PM in 2013. The NOx and PM emissions from AM were equivalent to 17% and 22% of total on-road traffic emissions in Anhui. Among nine types of AM considered, rural vehicles are the largest contributors to fuel consumption (31%) and air emissions (33–45%).

Global emission, atmospheric transport and deposition trends of microplastics originating from road traffic

&lt;p&gt;Since the first reports on the presence of plastic debris in the marine environment in the early 70s (1), plastics have been steadily accumulating in the environment. The global production of plastics in 2019 reached 368 Mt (from 311 Mt in 2014 and 225 Mt in 2004), with the largest portion produced in Asia (51%) (2), whereas 10% is believed to end into the sea every year (3). As a result, plastics have been confirmed today in several freshwater (4), and terrestrial (5) ecosystems; they fragment into microplastics (MPs, 1 &amp;#181;m to 5 mm) (6) and nanoplastics (&lt;1&amp;#181;m) (7) via physical processes (8). MP present has been now confirmed from the Alps (9) and the Pyrenees (10), as far as Antarctica (11) and the high Arctic (9). Consequently, MPs have been found to&lt;br&gt;affect coral reefs (12), marine (13) and terrestrial animals (14). Schwabl et al. (15) detected them in human stool, while a recent study by Ragusa et al. (16) reported that MPs were even found in all placental portions.&lt;br&gt;A smaller fraction of MPs originates from road traffic emissions (17). Kole et al. (18) reported global average emissions of tire wear particles (TWPs) of 0.81 kg year-1 per capita, about 6.1 million tonnes (~1.8% of total plastic production). Emissions of brake wear particles (BWPs) add another 0.5 million tonnes. TWPs and BWPs are produced via mechanical abrasion and corrosion (19). Here, we present global trends in emissions, transport and deposition of road MPs.&lt;/p&gt;&lt;p&gt;References:&lt;br&gt;1. Colton, J. B., et al. Science (80). 185, 491&amp;#8211;497 (1974).&lt;br&gt;2. PlasticsEurope. https://www.plasticseurope.org/en/resources/market-data (2019).&lt;br&gt;3. Mattsson, K., et al. Impacts 17, 1712&amp;#8211;1721 (2015).&lt;br&gt;4. Blettler, M. C. M., et al.Water Res. 143, 416&amp;#8211;424 (2018).&lt;br&gt;5. Chae, Y. &amp; An, Y. J. Environ. Pollut. 240, 387&amp;#8211;395 (2018).&lt;br&gt;6. Peeken, I. et al. Nat. Commun. 9, (2018).&lt;br&gt;7. Wagner, S. &amp; Reemtsma, T. Nat. Nanotechnol. 14, 300&amp;#8211;301 (2019).&lt;br&gt;8. Gewert, B., et al. Environ. Sci. Process. Impacts 17, 1513&amp;#8211;1521 (2015).&lt;br&gt;9. Bergmann, M. et al. Sci. Adv. 5, 1&amp;#8211;11 (2019).&lt;br&gt;10. Allen, S. et al. Nat. Geosci. 12, 339&amp;#8211;344 (2019).&lt;br&gt;11. Gonz&amp;#225;lez-Pleiter, M. et al. Mar. Pollut. Bull. 161, 1&amp;#8211;6 (2020).&lt;br&gt;12. Lamb, J. B. et al. P Science (80-. ). 359, 460&amp;#8211;462 (2018).&lt;br&gt;13. Wilcox, C., et al. Sci. Rep. 8, 1&amp;#8211;11 (2018).&lt;br&gt;14. Harne, R. J. Anim. Res. 383&amp;#8211;386 (2019) doi:10.30954/2277-940x.02.2019.25.&lt;br&gt;15. Schwabl, P. et al. Ann. Intern. Med. 171, 453&amp;#8211;457 (2019).&lt;br&gt;16. Ragusa, A. et al. Environ. Int. 146, 106274 (2021).&lt;br&gt;17. Schwarz, A. E., et al. Mar. Pollut. Bull. 143, 92&amp;#8211;100 (2019).&lt;br&gt;18. Jan Kole, P., et al. Int. J. Environ. Res. Public Health 14, 1&amp;#8211;4 (2017).&lt;br&gt;19. Penka&amp;#322;a, M., et al. Environments 5, 9 (2018).&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Downscaling system for modelling of atmospheric composition on regional, urban and street scales

In this study, the downscaling modelling chain for prediction of weather and atmospheric composition is described and evaluated against observations. The chain consists of interfaced models for forecasting at different spatio-temporal scales and run in a semi-operational mode. The forecasts were performed for European (EU) regional and Danish (DK) sub-regional/urban-scales by the offline coupled numerical weather prediction HIRLAM and atmospheric chemical transport CAMx models, and for Copenhagen city/street scale – by the online coupled computational fluid dynamics M2UE model. The results showed elevated NOx and lowered O3 concentrations over major urban, industrial and transport land/water routes in both the EU and DK domain forecasts. The O3 diurnal cycle predictions in both these domains were equally good, although O3 values were closer to observations for Denmark. At the same time, the DK forecast of NOx levels was more biased (with better prediction score of the diurnal cycle) than EU forecast, indicating a necessity to adjust emission rates. Further downscaling to the street level (Copenhagen) indicated that the NOx pollution was 2-fold higher on weekend and more than 5 times higher during working day with high pollution episode. Despite of high uncertainty in road traffic emissions, the street-scale model captured well the NOx diurnal cycle and onset of elevated pollution episode. The demonstrated downscaling system could be used in future online integrated meteorology and air quality research and operational forecasting, as well as applied for impact assessments on environment, population and decision making for emergency preparedness and safety measures planning.

Two lichens differing in element concentrations have similar spatial patterns of element concentrations responding to road traffic and soil input

Two epiphytic lichens (Xanthoria alfredii, XAa; X. ulophyllodes, XAu) and soil were sampled at three sites with varied distances to a road in a semiarid sandland in Inner Mongolia, China and analyzed for concentrations of 42 elements to assess the contribution of soil input and road traffic to lichen element burdens, and to compare element concentration differences between the two lichens. The study showed that multielement patterns, Fe:Ti and rare earth element ratios were similar between the lichen and soil samples. Enrichment factors (EFs) showed that ten elements (Ca, Cd, Co, Cu, K, P, Pb, S, Sb, and Zn) were enriched in the lichens relative to the local soil. Concentrations of most elements were higher in XAu than in XAa regardless of sites, and increased with proximity to the road regardless of lichen species. These results suggested that lichen element compositions were highly affected by soil input and road traffic. The narrow-lobed sorediate species were more efficient in particulate entrapment than the broad-lobed nonsorediate species. XAa and XAu are good bioaccumulators for road pollution in desert and have similar spatial patterns of element concentrations for most elements as response to road traffic emissions and soil input.