scholarly journals Urban Air Quality Impacts of Distributed Generation

2003 ◽  
Author(s):  
Marc Medrano ◽  
Jack Brouwer ◽  
G. S. Samuelsen ◽  
Marc Carreras ◽  
Donald Dabdub
Keyword(s):  
Air Quality ◽  
Distributed Generation ◽  
Atmospheric Chemistry ◽  
Base Case ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Case Scenario ◽  
Temporal Perspective ◽  
Base Case Scenario

Distributed Energy Resources (DER) have the potential to meet a significant portion of increased power demands of the future. DER applications can potentially provide benefits in electrical reliability and power quality, in addition to reducing total energy costs in combined cooling, heating and power (CHP) applications. However, the shift from a central generation paradigm to distributed generation results in different emissions characteristics and profiles from both a spatial and temporal perspective. Distributed generation is characterized by many sparsely distributed stationary sources within an urban air-shed compared to central generation where emissions sources are much larger, but typically located outside the air-shed in more remote locations. As a result, high market adoption of fuel-driven (non-renewable) distributed generation (DG) technologies, such as reciprocating engines and microturbines, may influence the air quality within a region. The present paper estimates air quality impacts for a representative distributed generation scenario in the South Coast Air Basin (SoCAB) of California. Simulations are based on the year 2010 with comparison to a base case scenario with no DG emissions. The DG scenarios are developed for a reasonable percentage of power met by DG, representative spatial distribution and temporal operation, and a mix of DG technologies and emissions factors. The resultant emissions inventory for each DG scenario is then provided as input to a three-dimensional air quality model including detailed atmospheric chemistry and transport for simulation of the SoCAB. Preliminary air quality results suggest that there will be an air quality impact, that the impacts will not be uniform throughout the air-shed, and that individual criteria pollutant concentrations may either rise or fall with the introduction of DG.

Sensitivity of a Complex Urban Air Quality Model to Input Data

1981 ◽  
Vol 20 (9) ◽  
pp. 1020-1040 ◽  
Author(s):  
Christian Seigneur ◽  
Thomas W. Tesche ◽  
Philip M. Roth ◽  
Larry E. Reid
Keyword(s):  
Air Quality ◽  
Input Data ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model

A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model

2018 ◽  
Vol 105 ◽  
pp. 132-152 ◽  
Author(s):  
Maxime Beauchamp ◽  
Laure Malherbe ◽  
Chantal de Fouquet ◽  
Laurent Létinois ◽  
Frédéric Tognet
Keyword(s):  
Air Quality ◽  
Polynomial Approximation ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model

scholarly journals Solar “brightening” impact on summer surface ozone between 1990 and 2010 in Europe – a model sensitivity study of the influence of the aerosol–radiation interactions

2018 ◽  
Vol 18 (13) ◽  
pp. 9741-9765 ◽  
Author(s):  
Emmanouil Oikonomakis ◽  
Sebnem Aksoyoglu ◽  
Martin Wild ◽  
Giancarlo Ciarelli ◽  
Urs Baltensperger ◽  
...  
Keyword(s):  
Air Quality ◽  
Surface Ozone ◽  
Biogenic Emissions ◽  
Base Case ◽  
Air Quality Model ◽  
Quality Model ◽  
Ozone Concentrations ◽  
Photolysis Rates ◽  
Ozone Trends ◽  
The Impact

Abstract. Surface solar radiation (SSR) observations have indicated an increasing trend in Europe since the mid-1980s, referred to as solar “brightening”. In this study, we used the regional air quality model, CAMx (Comprehensive Air Quality Model with Extensions) to simulate and quantify, with various sensitivity runs (where the year 2010 served as the base case), the effects of increased radiation between 1990 and 2010 on photolysis rates (with the PHOT1, PHOT2 and PHOT3 scenarios, which represented the radiation in 1990) and biogenic volatile organic compound (BVOC) emissions (with the BIO scenario, which represented the biogenic emissions in 1990), and their consequent impacts on summer surface ozone concentrations over Europe between 1990 and 2010. The PHOT1 and PHOT2 scenarios examined the effect of doubling and tripling the anthropogenic PM2.5 concentrations, respectively, while the PHOT3 investigated the impact of an increase in just the sulfate concentrations by a factor of 3.4 (as in 1990), applied only to the calculation of photolysis rates. In the BIO scenario, we reduced the 2010 SSR by 3 % (keeping plant cover and temperature the same), recalculated the biogenic emissions and repeated the base case simulations with the new biogenic emissions. The impact on photolysis rates for all three scenarios was an increase (in 2010 compared to 1990) of 3–6 % which resulted in daytime (10:00–18:00 Local Mean Time – LMT) mean surface ozone differences of 0.2–0.7 ppb (0.5–1.5 %), with the largest hourly difference rising as high as 4–8 ppb (10–16 %). The effect of changes in BVOC emissions on daytime mean surface ozone was much smaller (up to 0.08 ppb, ∼ 0.2 %), as isoprene and terpene (monoterpene and sesquiterpene) emissions increased only by 2.5–3 and 0.7 %, respectively. Overall, the impact of the SSR changes on surface ozone was greater via the effects on photolysis rates compared to the effects on BVOC emissions, and the sensitivity test of their combined impact (the combination of PHOT3 and BIO is denoted as the COMBO scenario) showed nearly additive effects. In addition, all the sensitivity runs were repeated on a second base case with increased NOx emissions to account for any potential underestimation of modeled ozone production; the results did not change significantly in magnitude, but the spatial coverage of the effects was profoundly extended. Finally, the role of the aerosol–radiation interaction (ARI) changes in the European summer surface ozone trends was suggested to be more important when comparing to the order of magnitude of the ozone trends instead of the total ozone concentrations, indicating a potential partial damping of the effects of ozone precursor emissions' reduction.

Estimates of Uncertainty in Urban Air Quality Model Predictions

2005 ◽  
pp. 483-491
Author(s):  
Sotiris Vardoulakis ◽  
Bernard Fisher ◽  
Norbert Gonzalez-Flesca ◽  
Koulis Pericleous
Keyword(s):  
Air Quality ◽  
Urban Air Quality ◽  
Urban Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Model Predictions

Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

2020 ◽  
Author(s):  
Philipp Schneider ◽  
Nuria Castell ◽  
Paul Hamer ◽  
Sam-Erik Walker ◽  
Alena Bartonova
Keyword(s):  
Sensor Networks ◽  
Air Quality ◽  
High Resolution ◽  
Air Pollutants ◽  
Low Cost ◽  
Urban Air Quality ◽  
Urban Air ◽  
Sensor Systems ◽  
Air Quality Model ◽  
Quality Model

<p>One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM<sub>2.5</sub> observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.</p><p>In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.</p><p>While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.</p><p> </p><p>Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.</p>

scholarly journals Error apportionment for atmospheric chemistry-transport models: a new approach to model evaluation

2016 ◽  
Author(s):  
E. Solazzo ◽  
S. Galmarini
Keyword(s):  
Air Quality ◽  
Correlation Coefficient ◽  
Atmospheric Chemistry ◽  
Model Evaluation ◽  
Surface Ozone ◽  
Model Complexity ◽  
Air Quality Model ◽  
Quality Model ◽  
Causes Of Errors ◽  
Bias Variance

Abstract. In this study, methods are proposed to diagnose the causes of errors in air quality (AQ) modelling systems. We investigate the deviation between modelled and observed time series of surface ozone through a revised formulation for breaking down the mean square error (MSE) into bias, variance, and the minimum achievable MSE (mMSE). The bias measures the accuracy and implies the existence of systematic errors and poor representation of data complexity, the variance measures the precision and provides an estimate of the variability of the modelling results in relation to the observed data, and the mMSE reflects unsystematic errors and provides a measure of the associativity between the modelled and the observed fields through the correlation coefficient. Each of the error components is analysed independently and apportioned to resolved process based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) and as a function of model complexity. The apportionment of the error is applied to the AQMEII (Air Quality Model Evaluation International Initiative) group of models, which embrace the majority of regional AQ modelling systems currently used in Europe and North America. The proposed technique has proven to be a compact estimator of the operational metrics commonly used for model evaluation (bias, variance, and correlation coefficient), and has the further benefit of apportioning the error to the originating timescale, thus allowing for a clearer diagnosis of the process that caused the error.

scholarly journals Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown

2016 ◽  
Author(s):  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Christian Hogrefe ◽  
Gabriele Curci ◽  
Ummugulsum Alyuz ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Comparison ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Strong Impact ◽  
Air Quality Model ◽  
Quality Model ◽  
Transport Modelling

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

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

2011 ◽  
Vol 11 (14) ◽  
pp. 7355-7373 ◽  
Author(s):  
S. Aksoyoglu ◽  
J. Keller ◽  
I. Barmpadimos ◽  
D. Oderbolz ◽  
V. A. Lanz ◽  
...  
Keyword(s):  
Air Quality ◽  
Organic Aerosol ◽  
Oxidation Products ◽  
Base Case ◽  
Chemical Compositions ◽  
Aerosol Formation ◽  
Air Quality Model ◽  
Quality Model ◽  
Aerosol Composition ◽  
Sensitivity Tests

Abstract. This paper describes aerosol modelling in Europe with a focus on Switzerland during summer and winter periods. We modelled PM2.5 (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 NOx and NH3 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 NOx-sensitive. In summer, effects of NOx and NH3 emission reductions on aerosol concentrations are predicted to be lower mostly due to lower ammonium nitrate concentrations. In general, the sensitivity to NH3 emissions is weaker in summer due to higher NH3 emissions.

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