A Comprehensive Emission Inventory of Bbiogenic Volatile Organic Compounds in Europe: Improved Seasonality and Land-cover

Biogenic volatile organic compounds (BVOC) emitted from vegetation are important for the formation of secondary pollutants such as ozone and secondary organic aerosols (SOA) in the atmosphere. Therefore, BVOC emission are an important input for air quality models. To model these emissions with high spatial resolution, the accuracy of the underlying vegetation inventory is crucial. We present a BVOC emission model that accommodates different vegetation inventories and uses satellite-based measurements of greenness instead of pre-defined vegetation periods. This approach to seasonality implicitly treats effects caused by water or nutrient availability, altitude and latitude on a plant stand. Additionally, we test the influence of proposed seasonal variability in enzyme activity on BVOC emissions. In its present setup, the emission model calculates hourly emissions of isoprene, monoterpenes, sesquiterpenes and the oxygenated volatile organic compounds (OVOC) methanol, formaldehyde, formic acid, ethanol, acetaldehyde, acetone and acetic acid. In this study, emissions based on three different vegetation inventories are compared with each other and diurnal and seasonal variations in Europe are investigated for the year 2006. Two of these vegetation inventories require information on tree-cover as an input. We compare three different land-cover inventories (USGS GLCC, GLC2000 and Globcover 2.2) with respect to tree-cover. The often-used USGS GLCC land-cover inventory leads to a severe reduction of BVOC emissions due to a potential miss-attribution of broad-leaved trees and reduced tree-cover compared to the two other land-cover inventories. To account for uncertainties in the land-cover classification, we introduce land-cover correction factors for each relevant land-use category to adjust the tree-cover. The results are very sensitive to these factors within the plausible range. For June 2006, total monthly BVOC emissions decreased up to −27% with minimal and increased up to +71% with maximal factors, while in January 2006, the changes in monthly BVOC emissions were −54 and +56% with minimal and maximal factors, respectively. The new seasonality approach leads to a reduction in the annual emissions compared with non-adjusted data. The strongest reduction occurs in OVOC (up to −32 %), the weakest in isoprene (as little as −19 %). If also enzyme seasonality is taken into account, however, isoprene reacts with the steepest decrease of annual emissions, which are reduced by −44% to −49 %, annual emissions of monoterpenes reduce between −30 and −35 %. The sensitivity of the model to changes in temperature depends on the climatic zone but not on the vegetation inventory. The sensitivity is higher for temperature increases of 3K (+31% to +64 %) than decreases by the same amount (−20 to −35 %). The climatic zones “Cold except summer” and “arid” are most sensitive to temperature changes in January for isoprene and monoterpenes, respectively, while in June, “polar” is most sensitive to temperature for both isoprene and monoterpenes. Our model predicts the oxygenated volatile organic compounds to be the most abundant fraction of the annual European emissions (3571–5328 Gg yr−1), followed by monoterpenes (2964–4124 Gg yr−1), isoprene (1450–2650 Gg yr−1) and sesquiterpenes (150–257 Gg yr−1). We find regions with high isoprene emissions (most notably the Iberian Peninsula), but overall, oxygenated VOC dominate with 43–45% (depending on the vegetation inventory) contribution to the total annual BVOC emissions in Europe. Isoprene contributes between 18–21 %, monoterpenes 33–36% and sesquiterpenes contribute 1–2 %.We compare the concentrations of biogenic species simulated by an air quality model with measurements of isoprene and monoterpenes in Hohenpeissenberg (Germany) for both summer and winter. The agreement between observed and modelled concentrations is better in summer than in winter. This can partly be explained with the difficulty to model weather conditions in winter accurately, but also with the increased anthropogenic influence on the concentrations of BVOC compounds in winter. Our results suggest that land-cover inventories used to derive tree-cover must be chosen with care. Also, uncertainties in the classification of land-cover pixels must be taken into account and remain high. This problem must be addressed together with the remote sensing community. Our new approach using a greenness index for addressing seasonality of vegetation can be implemented easily in existing models. The importance of OVOC for air quality should be more deeply addressed by future studies, especially in smog chambers. Also, the fate of BVOC from the dominant region of the Iberian Peninsula should be studied more in detail

Aerosol modelling in Europe with a focus on Switzerland during summer and winter episodes

This paper describes aerosol modelling in Europe with a focus on Switzerland during summer and winter periods. We modelled PM<sub>2.5</sub> (particles smaller than 2.5 μm in aerodynamic diameter) for one summer and two winter periods in years 2006 and 2007 using the CAMx air quality model. The meteorological fields were obtained from MM5 simulations. The modelled wind speeds during some low-wind periods, however, had to be calibrated with measurements to use realistic input for the air quality model. The detailed AMS (aerosol mass spectrometer) measurements at specific locations were used to evaluate the model results. In addition to the base case simulations, we carried out sensitivity tests with modified aerosol precursor emissions, air temperature and deposition. Aerosol concentrations in winter 2006 were twice as high as those in winter 2007, however, the chemical compositions were similar. CAMx could reproduce the relative composition of aerosols very well both in the winter and summer periods. Absolute concentrations of aerosol species were underestimated by about 20 %. Both measurements and model results suggest that organic aerosol (30–38 %) and particulate nitrate (30–36 %) are the main aerosol components in winter. In summer, organic aerosol dominates the aerosol composition (55–57 %) and is mainly of secondary origin. The contribution of biogenic volatile organic compound (BVOC) emissions to the formation of secondary organic aerosol (SOA) was predicted to be very large (>95 %) in Switzerland. The main contributors to the modelled SOA concentrations were oxidation products of monoterpenes and sesquiterpenes as well as oligomerization of oxidized compounds. The fraction of primary organic aerosol (POA) derived from measurements was lower than the model predictions indicating the importance of volatility of POA, which has not yet been taken into account in CAMx. Sensitivity tests with reduced NO<sub>x</sub> and NH<sub>3</sub> emissions suggest that aerosol formation is more sensitive to ammonia emissions in winter in a large part of Europe. In Switzerland however, aerosol formation is predicted to be NO<sub>x</sub>-sensitive. In summer, effects of NO<sub>x</sub> and NH<sub>3</sub> emission reductions on aerosol concentrations are predicted to be lower mostly due to lower ammonium nitrate concentrations. In general, the sensitivity to NH<sub>3</sub> emissions is weaker in summer due to higher NH<sub>3</sub> emissions

Temporal patterns and trends of particulate matter over Portugal: a long-term analysis of background concentrations

Air quality management regarding PM concentrations in the atmosphere is a complex problem to tackle. In this paper, we aim to characterize the temporal patterns and trends of aerosol background levels over Portugal. Hourly data from the national air quality monitoring network, gathered from 2007 to 2016, is analyzed using statistical methods. Data from 20 monitoring stations was processed to prepare datasets with different time scales, and results were grouped by their type of surrounding area (urban, suburban, or rural). Urban and suburban background sites are characterized by strong seasonal patterns, with higher monthly mean concentrations in winter than in summer. In contrast, rural background PM10 concentrations are highest during August and September. This study suggests that urban background concentrations are significantly influenced by anthropogenic non-combustion sources, which contribute to the coarser aerosol fraction (PMc). PMc is about 3 μg m−3 higher during weekdays than during Sundays, at urban sites. However, there is no clear relationship between the value of the PM2.5/PMc ratio and the type of monitoring station. During the 10-year period of study, a decrease of 1.83, 3.58, and 4.89%/year was registered in PM10 concentrations at Portuguese rural, urban, and suburban areas, respectively. Despite the higher decrease at suburban monitoring stations, those sites present the highest 10-year mean PM10 concentrations. This work provides an import insight on temporal variations of PM10, PM2.5, and PMc concentrations over Portugal and summarizes trends through the last decade, contributing to the discussion on sources and processes influencing those concentrations.Thanks also are due to the Portuguese Agency for the Environment (APA) and the Regional Coordination and Development Commissions (CCDRs) for their effort in establishing and maintaining the air quality monitoring sites used in this investigation.publishe

One decade of parallel fine (PM_2.5) and coarse (PM₁₀–PM_2.5) particulate matter measurements in Europe: trends and variability

The trends and variability of PM₁₀, PM_2.5 and PM_coarse concentrations at seven urban and rural background stations in five European countries for the period between 1998 and 2010 were investigated. Collocated or nearby PM measurements and meteorological observations were used in order to construct Generalized Additive Models, which model the effect of each meteorological variable on PM concentrations. In agreement with previous findings, the most important meteorological variables affecting PM concentrations were wind speed, wind direction, boundary layer depth, precipitation, temperature and number of consecutive days with synoptic weather patterns that favor high PM concentrations. Temperature has a negative relationship to PM_2.5 concentrations for low temperatures and a positive relationship for high temperatures. The stationary point of this relationship varies between 5 and 15 °C depending on the station. PM_coarse concentrations increase for increasing temperatures almost throughout the temperature range. Wind speed has a monotonic relationship to PM_2.5 except for one station, which exhibits a stationary point. Considering PM_coarse, concentrations tend to increase or stabilize for large wind speeds at most stations. It was also observed that at all stations except one, higher PM_2.5 concentrations occurred for east wind direction, compared to west wind direction. Meteorologically adjusted PM time series were produced by removing most of the PM variability due to meteorology. It was found that PM₁₀ and PM_2.5 concentrations decrease at most stations. The average trends of the raw and meteorologically adjusted data are −0.4 μg m⁻³ yr⁻¹ for PM₁₀ and PM_2.5 size fractions. PM_coarse have much smaller trends and after averaging over all stations, no significant trend was detected at the 95% level of confidence. It is suggested that decreasing PM_coarse in addition to PM_2.5 can result in a faster decrease of PM₁₀ in the future. The trends of the 90th quantile of PM₁₀ and PM_2.5 concentrations were examined by quantile regression in order to detect long term changes in the occurrence of very large PM concentrations. The meteorologically adjusted trends of the 90th quantile were significantly larger (as an absolute value) on average over all stations (−0.6 μg m⁻³ yr⁻¹)

Influence of meteorology on PM₁₀ trends and variability in Switzerland from 1991 to 2008

Measurements of airborne particles with aerodynamic diameter of 10 μm or less (PM10) and meteorological observations are available from 13 stations distributed throughout Switzerland and representing different site types. The effect of all available meteorological variables on PM10 concentrations was estimated using Generalized Additive Models. Data from each season were treated separately. The most important variables affecting PM10 concentrations in winter, autumn and spring were wind gust, the precipitation rate of the previous day, the precipitation rate of the current day and the boundary layer depth. In summer, the most important variables were wind gust, Julian day and afternoon temperature. In addition, temperature was important in winter. A "weekend effect" was identified due to the selection of variable "day of the week" for some stations. Thursday contributes to an increase of 13% whereas Sunday contributes to a reduction of 12% of PM10 concentrations compared to Monday on average over 9 stations for the yearly data. The estimated effects of meteorological variables were removed from the measured PM10 values to obtain the PM10 variability and trends due to other factors and processes, mainly PM10 emissions and formation of secondary PM10 due to trace gas emissions. After applying this process, the PM10 variability was much lower, especially in winter where the ratio of adjusted over measured mean squared error was 0.27 on average over all considered sites. Moreover, PM10 trends in winter were more negative after the adjustment for meteorology and they ranged between −1.25 μg m−3 yr−1 and 0.07 μg m−3 yr−1. The adjusted trends for the other seasons ranged between −1.34 μg m−3 yr−1 and −0.26 μg m−3 yr−1 in spring, −1.40 μg m−3 yr−1 and −0.28 μg m−3 yr−1 in summer and −1.28 μg m−3 yr−1 and −0.11 μg m−3 yr−1 in autumn. The estimated trends of meteorologically adjusted PM10 were in general non-linear. The two urban street sites considered in the study, Bern and Lausanne, experienced the largest reduction in measured and adjusted PM10 concentrations. This indicates a verifiable effect of traffic emission reduction strategies implemented during the past two decades. The average adjusted yearly trends for rural, urban background and urban street stations were −0.37, −0.53 and −1.2 μg m−3 yr−1 respectively. The adjusted yearly trends for all stations range from −0.15 μg m−3 yr−1 to −1.2 μg m−3 yr−1 or −1.2% yr−1 to −3.3% yr−1