Compliance and port air quality features with respect to ship fuel switching regulation: a field observation campaign, SEISO-Bohai

Since 1 January 2017, ships berthed at the core ports of three designated “domestic emission control areas” (DECAs) in China should be using fuel with a sulfur content less than or equal to 0.5 %. In order to evaluate the impacts of fuel switching, a measurement campaign (SEISO-Bohai) was conducted from 28 December 2016 to 15 January 2017 at Jingtang Harbor, an area within the seventh busiest port in the world. This campaign included meteorological monitoring, pollutant monitoring, aerosol sampling and fuel sampling. During the campaign, 16 ship plumes were captured by the on-shore measurement site, and 4 plumes indicated the usage of high-SF (SF refers to the sulfur content of marine fuels). The average reduction of the mean ΔNOx∕ΔSO2 ratio from high-sulfur plumes (3.26) before 1 January to low-sulfur plumes (12.97) after 1 January shows a direct SO2 emission reduction of 75 %, consistent with the sulfur content reduction (79 %). The average concentrations of PM2.5 (particulate matter with a diameter less than 2.5 µm), NOx, SO2, O3 and CO during campaign were 147.85 µg m−3, 146.93, 21.91, 29.68 ppb and 2.21 ppm, respectively, among which NOx reached a maximum hourly concentration of 692.6 ppb, and SO2 reached a maximum hourly concentration of 165.5 ppb. The mean concentrations of carbonaceous and dominant ionic species in particles were 6.52 (EC – elemental carbon), 23.10 (OC – organic carbon), 22.04 (SO42-), 25.95 (NO3-) and 13.55 (NH4+) µg m−3. Although the carbonaceous species in particles were not significantly affected by fuel switching, the gas and particle pollutants in the ambient air exhibited clear and effective improvements due to the implementation of low-sulfur fuel. Comparison with the prevailing atmospheric conditions and a wind map of SO2 variation concluded a prompt SO2 reduction of 70 % in ambient air after fuel switching. Given the high humidity at the study site, this SO2 reduction will abate the concentration of secondary aerosols and improve the acidity of particulate matter. Based on the enrichment factors of elements in PM2.5, vanadium was identified as a marker of residual fuel ship emissions, decreasing significantly by 97.1 % from 309.9 ng m−3 before fuel switching to 9.1 ng m−3 after regulation, which indicated a crucial improvement due to the implementation of low-sulfur fuels. Ship emissions were proven to be significantly influential both directly and indirectly on the port environment and the coastal areas around Bohai Bay, where the population density reaches over 650 people per square kilometer. The results from this study report the positive impact of fuel switching on the air quality in the study region and indicate a new method for identifying the ship fuel type used by vessels in the area.

Air Pollution Forecasting Model Based on Chance Theory and Intelligent Techniques

A novel approach of weighted support vector regression (WSVR) technique with applied chance theory was proposed to build a robust forecasting model, called the chance weighted support vector regression (chWSVR) model. In order to forecast the particulate matter air pollutant of diameter less than 10 micrometers (PM10) one hour advance in the Greater Cairo Metropolitan Area (GCMA) in Egypt. The chance theory has advanced concepts pertinent to treat cases where both randomness and fuzziness play simultaneous roles at one time. The basic idea based on the proposed chWSVR model is assigning the chance weight value of the target variable, based on the chance theory, to its corresponding dataset point to become minimized in the objective function making that point more significant during the training process. Measuring data were collected and reprocessed from four monitoring stations located in GCMA and relative to the springs during the period from 2007 to 2010. The results of such model compared to similar ones built by other machine learning techniques, Random Forest and Bootstrap aggregating techniques. In all stations, comparing such models revealed that the proposed chWSVR model findings were promising in the forecasting of PM10 hourly concentration.

A framework for expanding aqueous chemistry in the Community Multiscale Air Quality (CMAQ) model version 5.1

This paper describes the development and implementation of an extendable aqueous-phase chemistry option (AQCHEM − KMT(I)) for the Community Multiscale Air Quality (CMAQ) modeling system, version 5.1. Here, the Kinetic PreProcessor (KPP), version 2.2.3, is used to generate a Rosenbrock solver (Rodas3) to integrate the stiff system of ordinary differential equations (ODEs) that describe the mass transfer, chemical kinetics, and scavenging processes of CMAQ clouds. CMAQ's standard cloud chemistry module (AQCHEM) is structurally limited to the treatment of a simple chemical mechanism. This work advances our ability to test and implement more sophisticated aqueous chemical mechanisms in CMAQ and further investigate the impacts of microphysical parameters on cloud chemistry. Box model cloud chemistry simulations were performed to choose efficient solver and tolerance settings, evaluate the implementation of the KPP solver, and assess the direct impacts of alternative solver and kinetic mass transfer on predicted concentrations for a range of scenarios. Month-long CMAQ simulations for winter and summer periods over the US reveal the changes in model predictions due to these cloud module updates within the full chemical transport model. While monthly average CMAQ predictions are not drastically altered between AQCHEM and AQCHEM − KMT, hourly concentration differences can be significant. With added in-cloud secondary organic aerosol (SOA) formation from biogenic epoxides (AQCHEM − KMTI), normalized mean error and bias statistics are slightly improved for 2-methyltetrols and 2-methylglyceric acid at the Research Triangle Park measurement site in North Carolina during the Southern Oxidant and Aerosol Study (SOAS) period. The added in-cloud chemistry leads to a monthly average increase of 11–18 % in cloud SOA at the surface in the eastern United States for June 2013.

A framework for expanding aqueous chemistry in the Community Multiscale Air Quality (CMAQ) model version 5.1

This paper describes the development and implementation of an extendable aqueous phase chemistry option (AQCHEM-KMT(I)) for the Community Multiscale Air Quality (CMAQ) modeling system, version 5.1. Here the Kinetic PreProcessor (KPP), version 2.2.3, is used to generate a Rosenbrock solver (Rodas3) to integrate the stiff system of ODEs that describe the mass transfer, chemical kinetics, and scavenging processes of CMAQ clouds. CMAQ's standard cloud chemistry module (AQCHEM) is structurally limited to the treatment of a simple chemical mechanism. This work advances our ability to test and implement more sophisticated aqueous chemical mechanisms in CMAQ and further investigate the impacts of microphysical parameters on cloud chemistry. Box model cloud chemistry simulations were performed to choose efficient solver and tolerance settings, evaluate the implementation of the KPP solver, and assess the direct impacts of alternative solver and kinetic mass transfer on predicted concentrations for a range of scenarios. Month-long CMAQ simulations for winter and summer periods over the U.S. reveal the changes in model predictions due to these cloud module updates within the full chemical transport model. While monthly average CMAQ predictions are not drastically altered between AQCHEM and AQCHEM-KMT, hourly concentration differences can be significant. With added in-cloud secondary organic aerosol (SOA) formation from biogenic epoxides (AQCHEM-KMTI), normalized mean error and bias statistics are slightly improved for 2-methyltetrols and 2-methylglyceric acid at the Research Triangle Park measurement site in North Carolina during the SOAS field campaign period. The added in-cloud chemistry leads to a monthly-average increase of 11–18 % in "cloud" SOA at the surface in the eastern U.S. for June 2013.

Modelling of a carbon removal biological aerated filter doing partial nitrification during large-scale secondary treatment

A wastewater biofiltration model is used to assess the potential of modelling plant-sized secondary carbon removal biofilter units. Two distinct datasets collected at the Seine-Centre biofiltration plant (Colombes, France) are used. The model is first calibrated on multiple grab samples taken at different heights inside the filter media. Data from 24 hour composite samples at the unit influent and effluent over a 2 year period are then simulated. Additional data are used to estimate hourly concentration profiles from composite samples in order to correctly use both composite and grab samples during modelling. The calibrated model is in most cases able to correctly predict the general nutrient behaviour for both datasets. The results of statistical scores such as the mean error and the mean absolute error are low for soluble components and remain correct for particles during years 2008–2009. Only one parameter set containing few heavily modified values is used to obtain these results. Modelling plant-sized biofilters appears to be practical and can be useful for easily evaluating plant optimization scenarios.

Wintertime peroxyacetyl nitrate (PAN) in the megacity Beijing: the role of photochemical and meteorological processes

Peroxyacetyl nitrate (PAN) is one of the key photochemical pollutants and acts as an important reservoir for the peroxyacetyl (PA) radical and nitrogen oxides (NOx) over cold and less polluted regions. Previous measurements of PAN in Asian megacities were scarce and mainly conducted for relatively short periods in summer. In this study, we present and analyze the measurements of PAN, O3, NOx, CO, and some meteorological variables, made at an urban site (CMA) in Beijing from 25 January to 22 March 2010. During the observations, the hourly concentration of PAN varied from 0.23 to 3.51 ppb, with an average of 0.70 ppb. Both PAN and O3 showed small but significant diurnal cycle, with PAN peaking around 17:00 LT, three hours later than O3. The observed concentration of PAN is well correlated with that of NOx but not O3. These phenomena indicate that the variations of the winter concentrations of PAN and O3 in urban Beijing are decoupled with each other. Wind conditions and transport of air masses exert very significant impacts on O3, PAN, and other species. The strong WNW-N winds caused elevated concentrations of surface O3 and lower concentrations of PAN, NOx, and CO. Weak winds from the other directions led to enhanced levels of PAN, NOx, and CO and decreased level of O3. Air masses arriving at our site originated either from the boundary layer over the highly polluted N-S-W sector or from the free troposphere over the W-N sector. The descending free-tropospheric air was rich in O3, with an average PAN/O3 ratio smaller than 0.031, while the boundary layer air over the polluted sector contained higher levels of PAN and primary pollutants, with an average PAN/O3 ratio of 0.11. These facts related with meteorological conditions, specifically the air transport conditions, can well explain the observed PAN-O3 decoupling. The impact of meso-scale transport is demonstrated using a case during 21–22 February 2010. In addition to transport, photochemical production is important to PAN in the winter boundary layer over Beijing. The PA concentration is estimated from the measurements of PAN and related variables. The estimated PA concentration for three days with stable atmospheric condition, 7 February, 23 February, and 11 March, are in the range of 0–0.012, 0–0.036, and 0–0.040 ppt, respectively. We found that both the formation reaction and thermal decomposition contributed significantly to PAN's variation. The results here suggest that even in the colder period, both photochemical production and thermal decomposition of PAN in the polluted boundary layer over Beijing are not negligible, with the production exceeding the decomposition.

Combining modeling and monitoring to study fecal contamination in a small rural catchment

The present study sought to identify Escherichia coli sources in a small catchment and to use the agro-hydrological model soil and water assessment tool (SWAT) to estimate their impact on river water quality. The innovative aspects of this research are to assess the hourly variations of fecal contamination and to take these variations into account in the model to provide a better evaluation of river quality. Thus, water samples were taken weekly at the river outlet (n = 4) and 24-h monitoring sessions were performed during low and high-flow periods (n = 74). E. coli variations were found to be primarily linked to rainfall and not to resuspension mechanisms. Subdaily fluctuations and deviations were ±0.33 log10 cfu/100 mL and ±0.70 log10 cfu/100 mL for dry (<3 mm/day) and wet (>3 mm/day) weather, respectively. After river flow calibration, all known pollution sources (septic systems, manure spreading, farm discharges) were introduced into SWAT. The model reproduced the fecal contamination in the river and the use of subdaily deviations allowed us to evaluate the simulation quality and compare grab samplings with simulated daily E. coli concentration, thus confirming that the performance of the model is better when additional information on hourly concentration variations is used.