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This study presents a manual synoptic climate classification for the East Coast of New England with an application to regional pollution. New England weather was classified into 9 all-inclusive weather types: Canadian High, Modified High, Gulf of Maine Return, New England High, Atlantic Return, Frontal Atlantic Return, Frontal Overrunning- Continen...
Citations
... Regional weather patterns vary in their propensity for producing thunderstorms and lightning. However, these weather patterns can also be associated with different transport directions and concentrations of air pollution (Power, 2003;Keim et al., 2005;Power et al., 2006;Sheridan et al., 2008;Diem et al., 2010;Fuchs et al., 2015). Although aerosols may correlate with meteorological conditions, by comparing two locations, one can logically delineate some of these relationships. ...
A multi-variable investigation of thunderstorm environments in two distinct geographic regions is conducted to assess the aerosol and thermodynamic environments surrounding thunderstorm initiation. 12-years of cloud-to-ground (CG) lightning flash data are used to reconstruct thunderstorms occurring in a 225 km radius centered on the Washington, D·C. and Kansas City Metropolitan Regions. A total of 196,836 and 310,209 thunderstorms were identified for Washington, D.C. and Kansas City, MO, respectively. Hourly meteorological and aerosol data were then merged with the thunderstorm event database.
Evidence suggests, warm season thunderstorm environments in benign synoptic conditions are considerably different in thermodynamics, aerosol properties, and aerosol concentrations within the Washington, D.C. and Kansas City regions. However, thunderstorm intensity, as measured by flash counts, appears regulated by similar thermodynamic-aerosol relationships despite the differences in their ambient environments. When examining thunderstorm initiation environments, there exists statistically significant, positive relationships between convective available potential energy (CAPE) and flash counts. Aerosol concentration also appears to be a more important quantity than particle size for lightning augmentation.
... Designing a distributed sensor network for Boston is challenging in part due to the highly variable meteorological patterns of New England that affect PM 2.5 concentrations. This variability is caused by New England's physical geography, including its coastal orientation, position within the prevailing westerlies, and presence of mountains, as well as its diverse climate patterns, such as large diurnal changes in temperature, droughts, heavy rainfall, and blizzards (Keim et al., 2005). In addition, the Boston area is predominately White with spatially dispersed nonwhite communities, making it difficult to optimize based on the race metric. ...
In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low‐cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low‐cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low‐cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost‐constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM 2.5 ) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM 2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM 2.5 extremes and increasing pollution monitoring in low‐income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low‐income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM 2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low‐cost sensors in less privileged communities.
... Designing a distributed sensor network for Boston is challenging in part due to the highly variable meteorological patterns of New England that affect PM2.5 concentrations. This variability is caused by New England's physical geography, including its coastal orientation, position within the prevailing westerlies, and presence of mountains, as well as its diverse climate patterns, such as large diurnal changes in temperature, droughts, heavy rainfall, and blizzards (Keim et al., 2005). In addition, the Boston area is predominately White with spatially dispersed nonwhite communities, making it difficult to optimize based on the race metric. ...
In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency (EPA) mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from one day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.
... Particulate matter (PM) has aroused worldwide concern over the past decade due to its significant adverse effects on visibility, public health and even the global climate Mimura et al., 2014;Keim et al., 2005). Therefore, investigating the emission sources of PM is of immense importance for emission control strategies. ...
A single particle source analysis was conducted for particulate matter and oxalic acid using a single particle aerosol mass spectrometer during a typical biomass burning period in Tianjin, China. The adaptive resonance theory 2 neural network algorithm was applied to analyze mass spectra data and generate 20 particle clusters. Size-resolved time series of the particle classes were put into an advanced three-way model (ABB, multi-size three-way model) for source apportionment. Seven sources were identified, including crustal dust, biomass burning, fossil fuel combustion, secondary organic, secondary nitrate and secondary sulfate. Oxalic acid containing particles accounted for 1.4% of the total detected particles during the overall sampling period, and the fraction increased to 2.6% during the biomass burning period. Oxalic acid was predominantly observed in the Na-K-EC, EC, EC-K, EC-aged, K, Levoglucosan and Fe particle classes. The mixing fraction of oxalic acid in these particle types, and their diurnal variation and size distribution patterns are related to different formation mechanisms. Source contributions to oxalic acid were apportioned, and secondary sulfate (49%), biomass burning (25%), vehicle exhaust (17%), and secondary organic sources (8%) may have contributed to the amount of oxalic acid-containing particles. The contribution of secondary sulfate sources follows the O3 concentration during the sampling period. This is likely because strong photochemical oxidation activity during the sampling period produces more oxalic acid precursors from Volatile organic compounds. Biomass burning also clearly contributed the number of oxalic acid particles, because precursors emitted by biomass burning can be quickly oxidized into oxalic acid, and more oxalic acid was formed during transportation. Fe particles may also play important role in oxalic acid deposition.
... High concentration of particulate matter is now the primary concern about air quality in China, which adversely affects climate and human health (Jimenez et al., 2009;Mimura et al., 2014;Keim et al., 2005). Coal accounts for 65.6% of all energy consumed (coal equivalent calculation) in China in 2014, and thermal power generation and heating supply consume the largest part of coal (Wang and Luo, 2017). ...
Size-resolved mass spectral features of three typical coal combustion source types were analyzed and compared. • Signals of OC, nitrate, sulfate, Si, Al and Ca are tracers for these source types, and are effective in distinguishing them. • Contributions of three source types were quantified in winter of North China with the mass spectral features obtained. In this study, samples of three typical coal combustion source types, including Domestic bulk coal combustion (DBCC), Heat supply station (HSS), and Power plant (PP) were sampled and large sets of their mass spectra were obtained and analyzed by SPAMS during winter in a megacity in China. A primary goal of this study involves determining representative size-resolved single particle mass spectral signatures of three source types that can be used in source apportionment activities. Chemical types describe the majority of the particles of each source type were extracted by ART-2a algorithm with distinct size characteristics, and the corresponding tracer signals were identified. Mass spectral signatures from three source types were different from each other, and the tracer signals were effective in distinguishing different source types. A high size-resolution source apportionment method were proposed in this study through matching sources' mass spectral signatures to particle spectra in a twelve days ambient sampling to source apportion the particles. Contributions of three source types got different size characteristics, as HSS source got higher contribution in smaller sizes, But PP source got higher contributions as size increased. Source contributions were also quantified during two typical haze episodes, and results indicated that HSS source (for central-heating) and DBCC source (for domestic heating and cooking) may contribute evidently to pollution formation.
... Classification methods are essentially divided into two categories: one is the subjective classification based on the ground surface or upper air flow features, and the other is to use statistical methods to classify various meteorological elements clustering. Both of the two methods are highly practical in the analysis field of atmospheric environment and have been successfully applied in many places in China and overseas (Kalkstein and Corrigan, 1986;Davis, 1988;Davis and Gay, 1993;Yarnal, 1993;Keim et al., 2005;Huth et al., 2008;Zhang et al., 2012;Bei et al., 2016). By using the subjective classification method, Keim et al. (2005) divided the weather situation over the east coast of New England into nine categories and studied the distribution characteristics of PM 2.5 concentration of each weather type. ...
... Both of the two methods are highly practical in the analysis field of atmospheric environment and have been successfully applied in many places in China and overseas (Kalkstein and Corrigan, 1986;Davis, 1988;Davis and Gay, 1993;Yarnal, 1993;Keim et al., 2005;Huth et al., 2008;Zhang et al., 2012;Bei et al., 2016). By using the subjective classification method, Keim et al. (2005) divided the weather situation over the east coast of New England into nine categories and studied the distribution characteristics of PM 2.5 concentration of each weather type. The results showed that the PM 2.5 concentration which corresponds to the Atlantic return flow type (high temperature and high humidity) is the highest. ...
... Numerous studies have shown PM 2.5 to be a cause of direct and indirect adverse effects to human health, atmospheric visibility and global climate change. (Jim enez et al., 2009;Keim et al., 2005;Mimura et al., 2014). Due to the evidence of these adverse effects, the World Health Organization (WHO) recommends a 24-h standard limitation of 25 mg m À3 for PM 2.5 (WHO, 2005). ...
PM2.5 variances have adverse impacts on human beings and the environment; therefore, source apportionment is very important and is a hot global topic. In this work, a new model called WALSPMF is proposed and evaluated for its accuracy. First, a synthetic test was carried out to compare the estimated source profile and contributions with the synthetic ones. Average absolute error (AAE) values were also calculated between the estimated and synthetic source contributions; most of the values were low (<15%), which indicated that the results of the WALSPMF model might be acceptable. Next, samples of PM2.5 were collected from a large harbour sampling site in China (Tanggu). The PM2.5 mean level was 110.63 μg m-3, with a range of 28.67 μg m-3-302.17 μg m-3. The ambient PM2.5 dataset was separately introduced into both the WALSPMF and EPAPMF 5.0 models to identify the possible sources and their contributions. Five source categories were extracted by the two models and can be identified in the following consistent order: coal combustion (33% for WALSPMF, 30% for EPAPMF 5.0), secondary nitrate (19% for WALSPMF, 21% for EPAPMF 5.0), crustal dust (18% for WALSPMF, 22% for EPAPMF 5.0), secondary sulphate (16% for WALSPMF, 15% for EPAPMF 5.0), and vehicle exhaust (14% for WALSPMF, 12% for EPAPMF 5.0). The positive results of multiple verifications suggested good performance of the WALSPMF model; thus, it is essential to put this new model forward as a way to potentially enrich the modern source apportionment technique.
... Impacts of ENSO on weather type frequency were investigated by McCabe and Muller (2002), who concluded that El Nino tends to enhance rainfall in Gulf Return, Frontal Gulf Return, and Frontal Overunning weather types, while La Nina conditions lead to an increase in Gulf Return weather and warmer and drier conditions in Louisiana. Keim et al. (2005) used the Muller weather type classification system and adapted it to New England. In an application of the New England classification system they found that the weather types were related to PM 2.5 concentrations. ...
This article provides a review of manual synoptic climate classification systems dating back the turn of the twentieth century. It begins with a survey of the Bergen School and their development of the midlatitude cyclone model. This model is then used as a basis for the other synoptic weather classification systems including the Hess–Brezowsky Grosswetterlagen system based in Germany, H.H. Lamb's system developed for the United Kingdom and Western Europe, and the Muller system developed for Louisiana and North-Central Gulf of Mexico Coast. More recent efforts seem to be centered more on automating these classical weather typing systems.
... These classifications depended greatly on the experience of the researcher to recognize important patterns (Yarnal, 1993;Huth et al., 2008). While development and application of manual classifications are still found in recent synoptic climatology (Keim et al., 2005), the methods of synoptic classification have evolved rapidly as computers advanced to facilitate analysis of large, complex datasets. A range of automated methods has emerged, including correlation-based methods (Lund, 1963), cluster analysis (Kalkstein et al., 1987), self-organizing maps (Hewitson and Crane, 2002), and fuzzy clusters (Bardossy et al., 1995). ...
... On the other hand, objective classifications sometimes identify significant types that experts may not distinguish. The main advantage of manual techniques is that the user controls the weather types chosen, thus can ensure the types represent the important patterns for the region (Keim et al., 2005). ...
... Additionally, by adding a Gulf Low weather type in the hybrid classification, it now includes some of the Frontal Gulf Return and Frontal Overunning days from the original Muller classification. Using guidance of Keim et al. (2005), one-way multivariate analysis of variance (MANOVA) tests were used on the data to determine if the differences in the mean weather properties among the hybrid weather types are significant. The number of rainfall hours, sky cover, and wind direction were excluded from the analysis because they are not continuous variables, and therefore violate one of the assumptions for a MANOVA test. ...
A hybrid synoptic classification procedure for classifying Louisiana weather types based on the manual Muller system is presented. This procedure produces a synoptic classification system for Louisiana that harnesses the strengths of both manual and automated classifications, while eliminating the weaknesses. The Muller weather types archive from 1981 to 2001 is used in conjunction with the National Center for Atmospheric Research (NCAR)/National Centers for Environmental Prediction (NCEP) Reanalysis dataset to develop sea level pressure composites for each Muller weather type. The composites are used as seeds in an automated correlation-based algorithm to generate weather types from 1981 to 2001. Results of the automated procedure are compared to the Muller weather type catalog. Despite systematic differences between the two classifications, including the inability of the hybrid procedure to identify frontal placement, the hybrid classification correctly matched the Muller weather type for 52% of the seed days. In addition, the automated catalog captured the seasonal distribution and interannual variability of the Muller types remarkably well. The hybrid synoptic weather classification system applied to weather properties at Shreveport and New Orleans showed significant differences between weather types. The automated procedure does not replicate the Muller weather type classification exactly; however, it is homogenous within itself and has value for describing the variability of surface weather in Louisiana. In fact, it is arguably advantageous for some applications, owing to its objectivity, speed, and reproducibility.
... Also on the basis of manual CTCs, Dayan and Levy (2005) and Makra et al. (2007) investigated the relationship between large-scale atmospheric circulation and PM 10 concentrations in Tel Aviv (Israel) and Szeged (Hungary), respectively. For manually derived circulation types (CTs) over the New England region (USA), Keim et al. (2005) detected distinctly different levels of particulate matter concentrations in Durham, New Hampshire (USA). A statistically significant influence of automatically derived large-scale CTs on local PM 10concentrations in Edinburgh (UK) has been found by Buchanan et al. (2002) applying non-parametric analysis of variance. ...
Atmospheric circulation affects local concentrations of particulate matter with an aerodynamic diameter of 10 μm or less (PM10) in different ways: Via the determination of local meteorological conditions favoring or suppressing the formation and the accumulation of PM10, and through its control on short– and long–range transport of particles and precursors. The quantitative assessment of the connections between the large–scale atmospheric circulation and local PM10 is relevant not only for the understanding of observed variations in PM10 concentrations. It is even more important for estimating the potential effects of projected future changes in large–scale atmospheric circulation on PM10. In this contribution, daily atmospheric circulation types (CTs), resulting from variants of three different classification methods, and their monthly occurrence frequencies have been utilized in three different downscaling approaches for estimating monthly indices of PM10 for the period 1980–2010 at 16 locations in Bavaria (Germany). All variants of approaches have been evaluated via a leave–one–out cross validation procedure in order to attain reliable performance ratings to detect the most suitable downscaling approaches. Results indicate that the highest performance of downscaling approaches is achieved in winter when the best performing models explain on average roughly 50% of the observed PM10 variance. From this it can be concluded that classification–based approaches are generally suitable for the downscaling of PM10, particularly during winter when PM10 concentrations in Bavaria reach maximum values. As preferable settings of the downscaling approaches, the usage of rather small spatial domains and a relatively high number of classes for circulation type classification and furthermore the utilization of multiple linear regression analyses or random forest analyses for relating CTs to PM10 have been ascertained. These findings provide the basis for further enhancements of the classification–based downscaling of monthly PM10 that will be realized in successive investigations.
