ASSESSMENT OF AIR QUALITY IMPACT OF PATRAS HARBOUR

Transcription

1 POSEIDON Pollution monitoring of ship emissions: an integrated approach for harbours of the Adriatic basin Priority 2: Environmental Protection Topic 1: Sources of land-based and sea-based pollution Deliverable 2.4 ASSESSMENT OF AIR QUALITY IMPACT OF PATRAS HARBOUR Partner P3 Laboratory of Atmospheric Physics University of Patras May

2 Working group: Prof. Athanassios Argiriou (University of Patras) Prof. Dimitrios Melas (Aristotle University of Thessaloniki) Dr. Athanasios Karagiannidis (University of Patras) M.Sc. Spiros Dimopoulos (University of Patras) M.Sc. Vasileios Salamalikis (University of Patras) 2

3 CONTENTS 1. INTRODUCTION OBSERVATIONAL APPROACH Area and Data Methodology Results MODELING APPROACH The modeling system Quantification of the impact of shipping on air quality Relative contribution of various emission sources to the total emissions January July Comparison between January and July period Surface Concentrations January July Comparison between the January and the July period DISCUSSION AND CONCLUSIONS Ackownledgements REFERENCES

4 1. INTRODUCTION POSEIDON project aims at the assessment and quantification of the impact of sea traffic and harbor activities on the atmospheric quality of four Mediterranean port cities, namely Venice, Brindisi, Rijeka and Patras. It also seeks to identify policy gaps and to support proposals of integrated common strategies and actions for the sustainable development of coastal areas in the Adriatic Sea. To accomplish these goals, the University of Patras is performing an analysis based on two different but complementary axes. The first axis is an observational, localized approach of the problem. Following a state-of-the-art methodology, the impact of sea traffic and harbor activities on surface particulate matter concentrations at the Port of Patras is assessed. The second axis is to address the issue on a regional scale. A state of the art modeling system is applied and produces results for evaluating the impact of maritime activities over the central and eastern Mediterranean area, which of course include the four project harbors. The present document describes the methodologies employed towards achieving the POSEIDON project goals and also illustrates the results for both the axes of analysis. The results are summarized and discussed along with some issues of major importance. 4

5 2. OBSERVATIONAL APPROACH The Chemical Process and Energy Research Institute (CPERI) at CERTH (Centre for Research & Technology, Hellas) is a non-profit research and technological development organization situated in Northern Greece. It owns and operates a Mobile Laboratory, which is able to measure ambient gas pollutants, using the Horiba AP-370 series analyzers, including CO, NO X and SO 2. Particulate pollutants are measured using three instruments from TSI Inc., the CPC 3776, SMPS 3736 and DustTrak DRX. The first two of these instruments focus in the nanoscale and measure number concentration and size distribution of particles from ~4nm. DustTrak DRX is the latest addition to the monitoring equipment and measures particle mass concentration, of three different size fractions (PM1, PM2.5 and PM10) simultaneously. A measuring campaign using the Mobile Laboratory was performed in the Port of Patras for a summer and a winter period. The first period extends from 25/08/2013 to 01/09/2013, while the second period extends from 19/01/2014 to 27/01/2014. The data collected during that campaign were used to estimate the contribution of maritime and harbor activities in the atmospheric pollution of the Port area of the city of Patras. The analysis was focused on the aerosol particles concentrations and total particle number Area and Data The CPERI/CERTH measuring campaign included stationary and mobile measurements. The route that was followed along with the four sites of stationary measurements is shown in Fig.1. In order to focus on the influence of maritime activities on the air quality of the new (south) port, only the observations of the stationary site of the new port are used. These observations are included in the days that are presented in Table 1. The data used consist of airborne particulate matter concentrations of 4 magnitude bins and the particulate number count (see Table 2). Table 1. Days of the CPERI/CERTH measuring campaign, utilized for the assessment of the influence of maritime and harbor activities on the air quality of the new port of Patras Summer Period Winter Period 24/8/ /1/ /8/ /1/ /8/ /1/ /8/ /1/ /8/ /1/ /8/ /1/2014 1/9/2013 5

6 Fig.1. Routes and stationary measuring sites of the Mobile Laboratory campaign Table 2.Particle diameter of each magnitude bin Name PM1 PM2.5 PM10 Total PM Particle Diameter 1μm 2.5μm 10μm 15μm As will became evident in the following paragraph, two more sets of data are necessary to perform the analysis. The first set comprises of wind speed and direction data, covering the same time period. These data are courtesy of the Institute for Environmental Research and Sustainable Development of the National Observatory of Athens that operates an automated meteorological station inside the port area. The second set comprises of ship traffic data that were kindly provided by the authorities of the Patras port. It should be noted that the pollutants and meteorology datasets had different time resolutions and they were finally scaled to 10 minutes averaged values. The ship traffic data included arrival and departure times for all ships that docked in the new port of Patras. Based on these times, the periods when every ship was considered to be approaching, departing, loading and unloading were calculated. 6

7 2.2. Methodology The contribution of maritime and harbor activities in the air pollution of the Patras Port area is estimated following the methodology proposed by Contini et al. (2011) and Donateo et al. (2014). The relative contribution (δ) of the maritime and harbor activities on a pollutant levels is computed following the formula: where: C C DP DSP P P P (1) C D F F C C DP is the average concentration in a specified wind direction considering periods potentially influenced by ship emissions. C DSP is the average concentrations not significantly influenced by ship emissions. C D is the average concentration in the specific wind sector. F P is the fraction of cases influenced by ship emissions. In order to define the wind sector that influence the air quality of the Port of Patras, the approach and departure routes of the ship were considered. Since almost every cargo or passenger ship approaches and departs from the port from the west, the western sector winds are expected to transport ship pollutant emissions over the port area (Fig.2). Another issue that had to be resolved was the duration of approach/departure and loading/unloading procedures of the ships. Based on information received by the port authorities and related scientific literature (Donateo et al., 2014), it was decided to set the average approach and departure time at 20 minutes and the average loading and unloading time at 1 hour. D 7

8 Fig.2. Example of a passenger ship route approaching the new Port of Patras Results Table 3 summarizes the results of the pre-described analysis. It is evident that the maritime and harbor activities are increasing the surface concentrations of airborne particulate matter and the increase of the particle mass, depending on the particulate matter size, may range from 3.3% to 3.9%. Regarding the particle number count the increase is about 11%. A very interesting result is that the fine particle concentrations (PM1 and PM2.5) present a bit higher increase due to the maritime and harbor activities compared to that of the PM10 levels. Table 3. Relative contribution of the maritime and harbor activities to the airborne particulate matter over the Port of Patras Load/Unload plus Approach/Departure PM1 PM2.5 PM10 Total (0.1 15μm) PNC 4%±2% 4%±1% 3%±1% 3%±1% 11%±4% 8

9 3. MODELING APPROACH To assess the impact of maritime on the air quality of the central and eastern Mediterranean, a modeling approach was followed. Pollutants concentrations were estimated while using the WRF-CAMx modeling system for two different emission scenarios. In the first scenario the natural and all anthropogenic emission sources data were included, while in the second scenario the shipping pollutant emissions were excluded. This approach is called zero-out method and aims to compute the concentrations differences between the two scenarios, which are attributed to the impact of maritime activities on the air quality. The modeling system was implemented for a winter time period, i.e. January 2012, and a summer time period, i.e. July The modeling system The modeling system used consisted of the Weather Research and Forecast - Advanced Research Weather model (WRF version 3.5.1) and the photochemical model Comprehensive Air Quality Model with Extensions (CAMx version 5.3). The WRF and CAMx models were employed over the modeling domains as those presented in Fig. 3 including the central and eastern Mediterranean area. The flowchart of the modeling system is presented in Fig. 4. Fig. 3. The WRF and CAMx modeling domains. 9

10 Fig. 4. The WRF-CAMx modeling system flow chart. The numerical model WRF (version 3.5.1) is a next-generation Numerical Weather Prediction modeling system developed to serve both operational forecasting and atmospheric research needs. The model's dynamics solver integrates the compressible non-hydrostatic Euler equations (Ooyama, 1990), which are formulated on the Arakawa C grid using a terrain following mass vertical coordinate system (Laprise, 1992). Microphysics processes were parameterized using the New Thompson et al scheme (Thompson et al., 2008), whereas convection was parameterized with the Grell-Devenyi ensemble scheme (Grell and Devenyi, 2002). Shortwave and longwave radiation processes were handled with the RRTMG shortwave scheme and the RRTMG longwave parameterization (Iacono et al 2008), respectively. The surface layer was parameterized using the Eta similarity scheme (Janjic, 2002), while for the planetary boundary layer the Mellor-Yamada-Janjic scheme parameterization (Janjic, 1994) was used. Land surface processes were handled with the Noah Land Surface Model (Tewari et al., 2004). A detailed description of the model can be found in Skamarock et al. (2008). The model develops initial and boundary conditions based on the reanalysis forecast of ECMWF in o spatial resolution. The projection is the Lambert Conformal, and model runs were performed in 6 km spatial resolution over the domain presented in Fig. 3. CAMx is an Eulerian photochemical dispersion model that allows for an integrated one-atmosphere assessment of gaseous and particulate air pollution over many scales ranging from sub-urban to continental. It is designed to unify all of the technical features required of state of the science air quality models into a single system. CAMx simulates the emission, dispersion, chemical reaction, and removal of pollutants in the troposphere by solving the pollutant continuity equation for each chemical species on a system of three-dimensional grid(s) (ENVIRON, 2010). CAMx was applied over a domain with 367 x 227 grid cells of 6km spatial resolution (Fig. 3). The grid consisted of 17 vertical layers extending up to about 10 km above ground level. 10

11 The CAMx gaseous and PM chemical boundary conditions were derived from results of the IFS-MOZART global modeling system produced in the framework of the EU FP7 project Monitoring Atmospheric Composition and Climate Interim Implementation (MACC II) (Morcrette et al., 2009). The gas-phase chemical mechanism employed was the 2005 version of Carbon Bond (CB05) (Yarwood et al., 2005). Aerosol processes were modeled using a static two-mode coarse/fine scheme for the representation of the particle size distribution. The aerosol aqueous inorganic chemistry was according to the RADM-AQ aqueous chemistry algorithm (Chang et al., 1987). The partitioning of the inorganic aerosol constituents between the gas and aerosol phases was performed using the ISORROPIA thermodynamic module (Nenes et al., 1998). The secondary organic aerosol formation/partitioning was performed with the use of the SOAP scheme (Strader et al., 1999). The anthropogenic emissions (CO, NOx, SO 2, NH 3, NMVOCs, PM10 and PM2.5) where extracted from the European scale anthropogenic emissions database prepared by The Netherlands Organization (TNO) for the reference year 2009 in the framework of the EU FP7 project Monitoring Atmospheric Composition and Climate Interim Implementation (MACC II) (Kuenen et al., 2014). Emission data were available on an annual basis and had 1/8 o x 1/16 o spatial resolution. These data were spatially allocated (in 6 km resolution), temporally analyzed (hourly values) and chemical speciated while using the MOSESS anthropogenic emission model (Markakis et al., 2013). The Natural Emission MOdel (NEMO) was used in order to calculate spatially (6 km) and temporally (hourly) resolved, gaseous and particulate natural emissions which were accounted for while running the photochemical model. NEMO has been developed by the Laboratory of Atmospheric Physics of the Department of Physics of the Aristotle University of Thessaloniki in order to estimate biogenic NMVOCs, sea salt and wind-blown dust emissions driven by the WRF meteorology (Liora et al., 2014; 2013; Poupkou et al., 2010; Markakis et al., 2009) Quantification of the impact of shipping on air quality Two model runs are performed for each examined period (January 2012 / July 2012). In the first scenario, the natural and all anthropogenic emission sources data were included, while in the second scenario the shipping pollutant emissions were excluded. The CAMx model produced hourly surface and upper air concentration fields for pollutants. The hourly CAMx data were averaged for each time period and emission scenario and the following the formulae was applied on the averaged surface data to represent the contribution of shipping emissions on the air quality in the study domain: Conno Conwith diff 100% (2) Con where Con with and Con no are the averaged surface concentrations with and without shipping emissions. with In this analysis NOx, SO 2, CO, PM10, PM2.5 and O 3 are examined regarding the impact of shipping on their surface concentrations. 11

12 3.3. Relative contribution of various emission sources to the total emissions. The emissions of the pollutants are strongly connected to their surface concentrations. The apportionment of these emissions to different sources gives a very strong indication about the importance of each source including shipping. The relative contribution of each anthropogenic source to the monthly emissions of each pollutant is presented following. Fig. 5 is an example map that illustrates the spatial distribution of total NOx emissions for the month of July. Concerning the ship emissions, being the main interest of the current analysis, their spatial distribution reveals that the highest values are in the southern part of the study area due to international ship traffic. Higher are also the emission values along the ship routes in the Adriatic/Ionian and in the Aegean seas. Fig. 5. Total NOx emissions in the modeling domain in July January Fig. 6 illustrates the relative contribution of the anthropogenic emission sources to the domain wide pollutants emissions for the month of January. Shipping has a very small contribution to the CO emissions. For NOx and SO 2, the contribution of shipping to the total emissions is quite high reaching 22.5% and 10% respectively. Finally, the contribution of the shipping to the particulate matter emissions is rather small (about 5% for PM10 and 6% for PM2.5). 12

13 SO 2 Fig. 6. Relative contribution of emission sources to the total emissions for January. 13

14 July As can be seen in Fig. 7, the contribution of shipping to the total CO emissions is about 2.6%. Almost 28 % of NOx emissions are associated to maritime activities, while for SO 2 this percentage is about 16.5%. PM10 emissions related to shipping are almost 10%. This percentage increases for the finer particles, reaching 13% for PM2.5. SO 2 Fig. 7. Relative contribution of various groups of emission sources to the total emissions for July. 14

15 Comparison between January and July period The shipping CO emissions are almost negligible compared to those from other sources in both months. Regarding NOx, the shipping contribution is very large in both months and is almost 5% higher during July. January and July shipping emissions of SO 2 are a significant percentage of the total SO 2 emissions. An increase of 6% in the contribution of the shipping emissions during July is recorded. Finally, maritime activities contribution in particulate matter emissions is higher during July, especially for the finer particles. On the basis of the above, the impact of shipping emissions on air quality should be more pronounced in July rather than in January Surface Concentrations In the following sections the impact of shipping on the air quality in the central and eastern Mediterranean according to the methodology presented in section 3.2 is presented January Fig. 8 illustrates the relative increase or decrease of pollutants surface monthly concentrations, when the shipping emissions are excluded from the model simulations (see also section 3.2). CO levels do not change significantly indicating that sea traffic has only a small impact on the determination of the CO surface concentrations. NOx on the other hand present a remarkably high decrease due to the exclusion of the maritime emissions. This decrease is over 90% along the main ship routes of the Mediterranean while for the Adriatic and Ionian Seas the maximum change may range between -30% to -50%. Over port cities and coastal areas the reduction is generally lower than 20%. Over the greater part of the continental areas, the impact is less pronounced (< -5%). The decrease of NOx surface concentrations lead to lower O 3 titration rates and therefore it has a direct impact on the surface O 3 concentrations. These concentrations present an increase of up to 10% in areas along the main routes of international shipping to the south of the domain. Over the Adriatic and Ionian Seas and most of the coastal areas however, the impact is very small (i.e. up to ±1%). SO 2 is significantly decreasing when the shipping emissions are omitted. Over the international shipping routes this decrease is more than 80% but for the Adriatic and Ionian Seas this percentage is lower than 60% and over the port cities and coastal areas it drops to below 10%. Airborne particulate matter levels (as expressed by PM2.5 and PM10 concentrations) are also affected by the exclusion of these emissions. PM2.5 surface concentrations reach a reduction of about 10% along the shipping routes, while the PM10 surface concentrations reduction is a bit more than 5%. Regarding the Adriatic and Ionian Seas, the reduction is below 5% and 3% for PM2.5 and PM10 respectively. Over most of the coastal areas the average concentration reduction is lower than 4% for PM2.5 and is lower than 3% for PM10. 15

16 Fig. 8a. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (January 2012). 16

17 Fig. 8b. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (January 2012). 17

18 Fig. 8c. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (January 2012). 18

19 Fig. 8a. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (January 2012). 19

20 The relative differences in the pollutants surface concentrations specifically for the Port of Patras for January of 2012 are presented in Σφάλμα! Λανθασμένη αναφορά σελιδοδείκτη στον εαυτό του.. CO and O 3 are practically unaffected by the maritime activities while NOx and SO 2 concentrations are reduced by almost 15% and 9% when the shipping emissions are absent in the simulations. Airborne particulate matter is also reduced, but to a smaller extent (-2.6% for PM2.5 and -2.1% for PM10). Table 4. Relative differences of pollutants surface concentrations over the Patras Port as a result of the exclusion of shipping emissions in CAMx runs (January 2012). CO NO NO2 NOx O3 SO2 PM2.5 PM10 0.0% -15.4% -14.6% -14.3% +0.3% -8.8% -2.6% -2.1% July The impact of shipping activities on the surface concentrations of CO during the July of 2012 is very small over the greater part of the domain, as can be seen in Fig. 9. NOx are substantially affected by the absence of such emissions and present a decrease that may be over 90%. This decrease is over 70% in the Adriatic and Ionian Seas and it is reduced towards the coasts, where it may be lower than 30%. O 3 is decreased over most of the examined area, most likely due to the reduced availability of NOx, which is important precursor of tropospheric O 3. In the Adriatic and Ionian Seas and coastal areas, O 3 is reduced down to -10% when shipping emissions are excluded in photochemical model runs. SO 2 is decreased by more than 90% over the main shipping routes of international shipping to the south of the domain and over 60% in the Adriatic and Ionian Seas but this decrease diminishes as we approach the coastline, where it can be less than 40%. PM2.5 and PM10 surface concentrations are reduced by more than 15% and 13% respectively over the shipping routes and more than 5% and 3% respectively in the Adriatic and Ionian Seas. Over the greater part of the coastal areas these reductions are in the order of 3% and less. 20

21 Fig. 9a. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (July 2012). 21

22 Fig. 9b. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (July 2012). 22

23 Fig. 9c. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (July 2012). 23

24 Fig. 9d. Relative differences in pollutant concentrations due to the exclusion of shipping emissions (July 2012). 24

25 At the port of Patras the shipping activities do not have any impact on CO surface concentrations (Table 5). NOx and SO 2 are reduced by more than 20% when the shipping emissions are excluded in CAMx runs, while PM2.5 and PM10 present a reduction of 3.4% and 2.5% respectively. O 3 is reduced by more than 4%. Table 5. Relative differences of pollutants surface concentrations over the Patras Port as a result of the exclusion of shipping emissions CAMx runs (July 2012). CO NO NO2 NOx O3 SO2 PM2.5 PM10 0.0% -18.2% -22.5% -21.6% -4.4% -24.7% -3.4% -2.5% Comparison between the January and the July period CO surface concentrations in the greater part of the domain are almost unaffected by the shipping activities for both January and July. Therefore, no seasonality is observed regarding the impact of such activities on the average CO surface concentrations. The impact on NOx, SO 2 and particulate matter (PM10 and PM2.5) levels over the maritime areas seems to be higher during the summer month, a fact that is primarily attributed to the higher NOx emissions from shipping (both in absolute values and relative contributions) due to higher ship traffic activity during the summer period. This impact is recorded over the coastal areas too. The impact of shipping emissions on O 3 concentrations in the months of January and July cannot be compared due to the complicated photochemical processes that determine the O 3 creation and destruction. The lack of NOx shipping emissions during the winter time leads to a clear increase of O 3 as a result of lower titration. During summer however, the exclusion of maritime activities emissions also reduces the NOx availability but this time the limited availability results in lower O 3 production and an overall negative impact on O 3 levels due to the absence of shipping emissions. 4. DISCUSSION AND CONCLUSIONS Summarizing the preceding analysis, the following conclusions can be drawn: Based on the observational approach that used the data collected at the new Port of Patras: o The maritime activities are increasing the surface concentrations of airborne particulate matter by about 3.5% regarding the particle mass and 11% regarding the particle number count. o The increase induced by the maritime and harbor activities is slightly higher for the fine particles (PM1 and PM2.5) rather than for the PM10. 25

26 Based on the modeling approach that covers most of the central and eastern Mediterranean domain: o The contribution of shipping to the total emissions of NOx, SO 2, PM2.5 and PM10 is increased in July compared to January. o CO maritime emissions are low compared to the total CO emission, especially for January. o NOx and SO 2 present a drastic decrease that can approach 100% over the Mediterranean Sea main routes of international shipping when the shipping emissions are excluded. The decrease is much smaller over the coastal areas, but it is still greater than 10%, indicating that maritime emissions have a non-negligible impact on the air quality of coastal areas and port cities. o Particulate matter surface concentrations are also influenced by the shipping emissions sources. PM10 and PM2.5 values are clearly decreased when these emissions are removed from the model runs. As expected, the influence is stronger in the open sea. o The impact of shipping emissions on the O 3 levels is more complicated compared to the other pollutants considering that O 3 is not a primary pollutant but it is formed secondarily in the atmosphere as a result of various chemical and physical processes. Over the greater part of the modeling domain, the lack of NOx shipping emissions in winter time results in a clear increase of O 3 due to lower NOx titration. In summertime however, the exclusion of shipping emissions in CAMx runs results in lower O 3 production due to reduced NOx availability. In the Adriatic and Ionian Seas and coastal areas, O 3 is reduced down to -10% when shipping emissions are excluded in photochemical model runs. o During the summer period the influence of shipping emissions on NOx s, SO 2, PM2.5 and PM10 surface concentrations is stronger, a fact that is attributed to the higher ship traffic during that period. o For the port of Patras, NOx surface concentrations are decreased by almost 15% due to exclusion of shipping emissions during January 2012 and over 20% during July SO 2 concentrations are decreased by 8% during January and 25% during July. The variation in O 3 levels is generally small, especially for January. The impact of shipping to CO surface concentrations is negligible for both months. PM10 concentrations are decreased by more than 2% in both months while PM2.5 are also decreased by the exclusion of ship traffic by over 3% during summer. The impact of ship traffic on PM10 and PM2.5 levels resulting from the application of the photochemical model CAMx is in agreement with the results of the observational analysis presented in this report. o The above results, especially for NOx and SO 2, suggest that the ship traffic emission are an important environmental issue for the central and eastern Mediterranean and for the city of Patras also and that they should be efficiently controlled and regulated in order to decrease their impact and improve the air quality both on regional but on city scale also. 26

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