Grid-based Air Research Articles

Hybrid Machine Learning System to Forecast Electricity Consumption of Smart Grid-Based Air Conditioners

This study develops a hybrid prediction system to forecast 1-day-ahead electricity consumption of air conditioners in office spaces. The hybrid system combines a linear autoregressive integrated moving average model and a nonlinear nature-inspired metaheuristic optimization-based prediction model. To evaluate the efficacy of the proposed system, a smart grid-based monitoring device was installed in an office space, which consists of smart meters, environmental monitoring sensors, infrared sensors, and fan adjustment systems. Data were retrieved to train and test the proposed system. Sensitivity analyses were performed to identify the optimal parameters of the model and inputs for future use. Evaluation results confirmed that the proposed hybrid system outperformed the conventional linear and nonlinear models, showing good agreement between predicted and actual electricity consumption of air conditioners. Particularly, the proposed system obtained the correlation coefficient R of 0.71 and total error rate of 4.8%. The hybrid system can facilitate facility managers in forecasting electricity consumption of air conditioners.

An approach to characterize within-grid concentration variability in air quality models

Abstract A new methodology to obtain subgrid air quality concentration variability (SGV) as distribution functions is described. Grid-based air quality models provide deterministic single value outcomes and thus do not represent concentration spatial details which can vary widely especially within urban and highly industrialized areas. While such spatial details can be obtained by running grid-based regional-scale air quality models at finer resolution or by employing local-scale models, such implementation may be impractical for performing a variety of applications, e.g., health exposure assessments. Strategically we propose a paradigm that can provide operational AQ models with supplemental estimates of SGV distributions as analytic functions provided on hourly and grid-by-grid bases. We illustrate and discuss this prototype implementation for a case study of benzene for several 12-km grid cells in the Wilmington, Delaware area. For this effort the contribution to SGV is limited to that from fine grid emissions distributions.

Using a sub-grid scale modeling approach to simulate the transport and fate of toxic air pollutants

Several air toxics are emitted from mobile sources on roadways and these emissions account for a significant fraction of the health risks to the population. In addition, health effect studies are now becoming more comprehensive and some account for the spatial heterogeneities of air pollutant concentration fields (as is the case near roadways). Standard models can simulate either the near-source concentration fields or the urban background, but no model can handle both spatial scales in the vicinity of roadways in a coherent and scientifically correct manner. Here, we present a model that provides such an integrated treatment by combining a grid-based air quality model of the urban background with a plume-in-grid representation of roadway emissions. The model is applied to simulate near-roadway concentrations due to emissions from a busy interstate highway in New York City. Qualitative comparisons with typical measured concentration profiles show that the model captures the observed features of toxic air pollutant concentrations near roadways.

An Assessment of the Emissions Inventory Processing Systems EMS-2001 and SMOKE in Grid-Based Air Quality Models

In the United States, emission processing models such as Emissions Modeling System-2001 (EMS-2001), Emissions Preprocessor System-Version 2.5 (EPS2.5), and the Sparse Matrix Operator Kernel Emissions (SMOKE) model are currently being used to generate gridded, hourly, speciated emission inputs for urban and regional-scale photochemical models from aggregated pollutant inventories. In this study, two models, EMS-2001 and SMOKE, were applied with their default internal data sets to process a common inventory database for a high ozone (O3) episode over the eastern United States using the Carbon Bond IV (CB4) chemical speciation mechanism. A comparison of the emissions processed by these systems shows differences in all three of the major processing steps performed by the two models (i.e., in temporal allocation, spatial allocation, and chemical speciation). Results from a simulation with a photochemical model using these two sets of emissions indicate differences on the order of ±20 ppb in the predicted 1-hr daily maximum O3 concentrations. It is therefore critical to develop and implement more common and synchronized temporal, spatial, and speciation cross-reference systems such that the processes within each emissions model converge toward reasonably similar results. This would also help to increase confidence in the validity of photochemical grid model results by reducing one aspect of modeling uncertainty.

TEMMS: an integrated package for modelling and mapping urban traffic emissions and air quality

The Traffic Emission Modelling and Mapping Suite (TEMMS) is a program designed to provide detailed estimates of vehicle emissions on urban road networks, and so act as a precursor to urban air quality modelling. TEMMS is a module of the “Quantifiable City”, a more extensive model designed to address questions relevant to urban sustainability. Within the Quantifiable City model, TEMMS interfaces with SATURN, a traffic assignment model, and the Airviro or ADMS–Urban pollutant dispersion models, to calculate spatially defined pollutant concentrations for given traffic, meteorological and stationary source emission inputs. TEMMS also contains an integral model, ROADFAC, which calculates emissions of gases and particulates from vehicles using SATURN traffic or vehicle count data. TEMMS integrates these models via a database exchanger, the MapInfo geographic information system, and a custom-built Windows-based graphical user interface, allowing modelling and mapping of link-based vehicle flow and emissions, and grid-based air quality. TEMMS applications include emission and air quality mapping, evaluation of associated transport policies and scenarios, and preparation of inputs to other (e.g., epidemiological) models. Within this context, TEMMS has generated considerable interest from potential end-users in Local Authorities and air quality management support services. Validation of the integrated model is discussed, and an example application of TEMMS to a large UK city presented.

Chemical oxidant mechanisms for air quality modeling: critical review

Chemical oxidant mechanisms suitable for use in grid-based air quality simulation models are reviewed. Included are (1) a lumped structure mechanism, known as the Carbon Bond Mechanism, that is used for both urban- and regional-scale modeling, (2) a lumped species mechanism, called the SAPRC mechanism, which was developed for urban-scale modeling, (3) two versions of a lumped species mechanism (the RADM and RACM) developed for regional-scale applications, and (4) a new mechanism now under development, called the Morphecule Mechanism, that uses a novel approach for representing organic species. Included in the review are descriptions of the mechanisms and the results of evaluating the mechanisms against smog chamber data. Because of the importance that smog chamber data play in the development and evaluation of chemical mechanisms, a description of the chamber data available for this purpose is also presented. The shortcomings of both the smog chamber data and the existing chemical mechanisms are described. These shortcomings include difficulties in characterizing chamber artifact effects and chamber photolysis rates, uncertainties associated with kinetic and mechanistic data used to develop the mechanisms, and the assumptions and approximations used to condense mechanisms to a size appropriate for use in air quality models. Recommendations are offered for research activities to reduce these uncertainties. An effort is also made to translate the uncertainties and information gaps associated with the mechanisms into uncertainties in ozone predictions and ozone control strategies.

Numerical advective schemes used in air quality models—sequential and parallel implementation

Abstract Six algorithms for solving the advection equation are compared to determine their suitability for use in photochemical air quality models. The algorithms tested are the Smolarkiewicz method, the Galerkin finite element method, the numerical method of lines, the accurate space derivative method (ASD), Bott method, and Emde method. Four algorithms for filtering the numerical noise produced when solving the advection equation are also compared. The algorithms are evaluated both on two test problems and in the CIT model. The Galerkin finite element and the ASD methods are implemented in the CIT in parallel computation. Results indicate that the ASD method, coupled with the Forester filter, produces the most accurate results. When the ASD transport solver is implemented in parallel, a speed-up of about 88 is achieved using, 256 processors. Furthermore, a new set of optimized Forester filter parameters for grid-based air quality models is determined.

An air monitoring network using continuous particle size distribution monitors: Connecting pollutant properties to visibility via Mie scattering calculations

Aerosol size distribution data collected by the Southern California Air Quality Study (SCAQS) air monitoring network have been combined with values of the aerosol refractive index estimated from filter-based determinations of aerosol chemical composition. The resulting data have been used to calculate the aerosol light scattering coefficient values in time series at four air monitoring sites via Mie theory. It was found that the time series of the measured light scattering coefficient values could be reproduced well by the visibility model in those cases where redundant nephelometer measurements were available to assure that the measured light scattering coefficient values were not in doubt. Based on this study, it appears practical to maintain a network of monitoring sites that can be used to acquire size-distributed aerosol data simultaneously over a large geographic region. Such data will be needed in the near future for evaluation and testing of grid-based air quality models for the formation and transport of size-distributed particulate matter. Such data can be used at present to connect pollutant properties to visual range via modeling calculations based on Mie theory.

Mathematical modeling and control of the dry deposition flux of nitrogen-containing air pollutants

An Eulerian grid-based air quality model has been modified to include a resistance-based dry deposition code. The magnitude and spatial distribution of the dry deposition flux of nitrogen-containing pollutants to the surface of the Los Angeles area was calculated as a function of land use. For August 1982 base case conditions, the dry deposition flux was 247 t of N per day (5 from NO, 49 from NO_2, 7 from PAN, 101 from HNO_3, 59 from NH_3, and 26 from NH4_NO_3), which corresponds to more than half of the daily NO_x emissions to the local atmosphere. The effects of emission controls on NO_x and hydrocarbon sources in Southern California as they existed in 1982 were examined. At the highest level of control studied (37% reactive hydrocarbon reduction, 61 % NO_x reduction), the nitrogen dry flux would be 174 t of N per day after control (2 from NO, 20 from NO_2, 7 from PAN, 58 from HNO_3, 75 from NH_3, and 12 from NH_4NO_3.