scholarly journals A critical assessment of the Burning Index in Los Angeles County, California

2007 ◽  
Vol 16 (4) ◽  
pp. 473 ◽  
Author(s):  
Frederic P. Schoenberg ◽  
Chien-Hsun Chang ◽  
Jon E. Keeley ◽  
Jamie Pompa ◽  
James Woods ◽  
...  
Keyword(s):  
Wind Speed ◽  
Los Angeles ◽  
Relative Humidity ◽  
Process Model ◽  
Los Angeles County ◽  
Wildfire Occurrence ◽  
Simple Alternative ◽  
Individual Variables ◽  
Burning Index ◽  
The Impact

The Burning Index (BI) is commonly used as a predictor of wildfire activity. An examination of data on the BI and wildfires in Los Angeles County, California, from January 1976 to December 2000 reveals that although the BI is positively associated with wildfire occurrence, its predictive value is quite limited. Wind speed alone has a higher correlation with burn area than BI, for instance, and a simple alternative point process model using wind speed, relative humidity, precipitation and temperature well outperforms the BI in terms of predictive power. The BI is generally far too high in winter and too low in fall, and may exaggerate the impact of individual variables such as wind speed or temperature during times when other variables, such as precipitation or relative humidity, render the environment ill suited for wildfires.

scholarly journals SIMULATION OF SOIL LIQUEFACTION DUE TO EARTHQUAKE LOADING USING THE UBC3D-PLM MODEL

2019 ◽  
Vol 7 (3) ◽  
pp. 39-44
Author(s):  
Armen Ter-martirosyan ◽  
Osman Ahmad
Keyword(s):  
Los Angeles ◽  
Process Model ◽  
Soil Mass ◽  
Seismic Effect ◽  
Soil Liquefaction ◽  
Elastic Behavior ◽  
Soil Behavior ◽  
Basic Step ◽  
Seismic Impact ◽  
The Impact

Liquefaction is a phenomenon in which the stiffness and strength of a soil are reduced as a result of seismic effect or other dynamic effects. Liquefaction was the basic reason of the big damages caused by many earthquakes around the world. The basic step in the processes of predicting the soil liquefaction is the modeling of soil behavior. At the present time, numerous soil models are presented. Nevertheless, only some of them can simulate this process. Model UBC3D-PLM is one of these models which can be used. In this paper, the possibilities of this model are considered by modeling on the PLAXIS software package the seismic impact on a building with its different heights. The actual data of Upland earthquake 1990 near Los Angeles city was used. Results of this simulation showed us the difference in the behavior of the soil mass under the impact of an earthquake compared with the elastic behavior, as well as showed us the necessary to use the UBC3D-PLM model to estimate the seismic impact.

The Witch-Hunt Narrative

2017 ◽  
Vol 32 (6) ◽  
pp. 956-966 ◽  
Author(s):  
Esther H. Gillies
Keyword(s):  
Social Issue ◽  
Sexual Abuse ◽  
Child Sexual Abuse ◽  
Los Angeles ◽  
Social Worker ◽  
Los Angeles County ◽  
The Witch ◽  
The Impact ◽  
Witch Hunt ◽  
Insight Into

Responding to Ross E. Cheit’s Witch-Hunt Narrative, this article is a commentary chronicling the emergence of child sexual abuse as a social issue in Los Angeles County in the 1980s. Based on the responses to child sexual abuse in Los Angeles County as experienced by one social worker during the McMartin years, it discusses the impact of the McMartin case on the identification and intervention in child sexual abuse cases and tracks the evolution and changes that took place in the 1980s and 1990s in Southern California. It offers some insight into a rationale for the denial of child sexual abuse which continues to this day.

scholarly journals Development of a Fire Weather Index Using Meteorological Observations within the Northeast United States

2016 ◽  
Vol 55 (2) ◽  
pp. 389-402 ◽  
Author(s):  
Michael J. Erickson ◽  
Joseph J. Charney ◽  
Brian A. Colle
Keyword(s):  
Logistic Regression ◽  
Wind Speed ◽  
Relative Humidity ◽  
Logistic Regression Model ◽  
Daily Maximum ◽  
Fire Weather ◽  
Weather Index ◽  
Fire Weather Index ◽  
Wildfire Occurrence ◽  
Binomial Logistic Regression

AbstractA fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

Transnational Twist: Pecuniary Remittances and the Socioeconomic Integration of Authorized and Unauthorized Mexican Immigrants in Los Angeles County

2005 ◽  
Vol 39 (1) ◽  
pp. 69-102 ◽  
Author(s):  
Enrico A. Marcelli ◽  
B. Lindsay Lowell
Keyword(s):  
United States ◽  
Los Angeles ◽  
Los Angeles County ◽  
Mexican Immigrants ◽  
The United States ◽  
Neighborhood Context ◽  
Immigrant Integration ◽  
Individual Characteristics ◽  
Community Context ◽  
The Impact

Annual U.S.-Mexico pecuniary remittances are estimated to have more than doubled recently to at least $10 billion – augmenting interest among policymakers, financial institutions, and transnational migrant communities concerning how relatively poor expatriate Mexicans sustain such large transfers and the impact on immigrant integration in the United States. We employ the 2001 Los Angeles County Mexican Immigrant Residency Status Survey (LAC-MIRSS) to investigate how individual characteristics and social capital traditionally associated with integration, neighborhood context, and various investments in the United States influenced remitting in 2000. Remitting is estimated to have been inversely related to conventional integration metrics and influenced by community context in both sending and receiving areas. Contrary to straight-line assimilation theories and more consistent with a transnational or nonlinear perspective, however, remittances are also estimated to have been positively related to immigrant homeownership in Los Angeles County and negatively associated with having had public health insurance such as Medicaid.

scholarly journals Quantifying the Effects of Climate Factors on Carbon Monoxide Poisoning: A Retrospective Study in Taiwan

2021 ◽  
Vol 9 ◽  
Author(s):  
Chien-Ho Wang ◽  
Shih-Chieh Shao ◽  
Kai-Cheng Chang ◽  
Ming-Jui Hung ◽  
Chen-Chang Yang ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Wind Speed ◽  
Relative Humidity ◽  
Negative Binomial ◽  
Carbon Monoxide Poisoning ◽  
Climate Factors ◽  
Co Poisoning ◽  
Subgroup Analyses ◽  
Incidence Risk ◽  
The Impact

Background: Carbon monoxide (CO) poisoning is the leading cause of poisoning death worldwide, but associations between CO poisoning and weather remain unclear.Objective: To quantify the influence of climate parameters (e.g., temperature, relative humidity, and wind speed) on the incidence risk of acute CO poisoning in Taiwan.Methods: We used negative binomial mixed models (NBMMs) to evaluate the influence of weather parameters on the incidence risk of acute CO poisoning. Subgroup analyses were conducted, based on the seasonality and the intentionality of acute CO poisoning cases.Results: We identified a total of 622 patients (mean age: 32.9 years old; female: 51%) with acute CO poisoning in the study hospital. Carbon monoxide poisoning was associated with temperature (beta: −0.0973, rate ratio (RR): 0.9073, p < 0.0001) but not with relative humidity (beta: 0.1290, RR: 1.1377, p = 0.0513) or wind speed (beta: −0.4195, RR: 0.6574, p = 0.0806). In the subgroup analyses, temperature was associated with the incidence of intentional CO poisoning (beta: 0.1076, RR: 1.1136, p = 0.0333) in spring and unintentional CO poisoning (beta: −0.1865, RR: 0.8299, p = 0.0184) in winter.Conclusion: Changes in temperature affect the incidence risk for acute CO poisoning, but the impact varies with different seasons and intentionality in Taiwan. Our findings quantify the effects of climate factors and provide fundamental evidence for healthcare providers to develop preventative strategies to reduce acute CO poisoning events.

scholarly journals Examine the impact of weather and ambient air pollutant parameters on daily case of COVID-19 in India

2020 ◽  
Author(s):  
Kousik Das ◽  
Nilanjana Das Chatterjee
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Ambient Air ◽  
List Type ◽  
Positive Relation ◽  
Weather Variables ◽  
Sensitive Analysis ◽  
Negative Relation ◽  
The Impact

AbstractThe present study presents a view on exploring the relationship pattern between COVID 19 daily cases with weather parameters and air pollutants in mainland India. We consider mean temperature, relative humidity, solar radiation, rainfall, wind speed, PM2.5, PM10, SO2, NO2 and CO as independent variable and daily COVID 19 cases as dependent variable for 18 states during 18th march to 30th April, 2020.After dividing the dataset for 0 to 10 day, 10 to 25 days and 0 to 44 days, the current study applied Akaike s Information Criteria (AIC) and Generalized Additive Model (GAM) to examine the kind of relationship between independent variables with COVID 19 cases. Initially GAM model result shows variables like temperature and solar radiation has positive relation (p<0.05) in 0 to 10 days study with daily cases. In 25 days dataset it significantly shows that temperature has positive relation above 23 degree centigrade, SO2 has a negative relationship and relative humidity has negative (between 30% to 45% and > 60%) and a positive relationship (45% to 60%) with COVID 19 cases (p=0.05). 44 days dataset has six parameters includes temperature as positive, relative humidity as negative (between 0 to 45%) and then positive (after >45%), NO2 as Positive (0 to 35 microgram/m3) followed by negative trend (after > 40 microgram/m3), SO2 and rainfall as negative relation. After sensitive analysis, it is found that weather variables like relative humidity, solar radiation and rainfall are more sensitive than temperature and wind speed. Whereas pollutants like NO2, PM2.5, PM10 and CO are more sensitive variables than SO2 in this study. In summary this study finds temperature, relative humidity, solar radiation, wind speed, SO2, PM2.5, and CO may be important factors associated with COVID 19 pandemic.Graphical AbstractHighlights➢There was a significant relationship between daily positive COVID-19 case with weather and pollution factors➢We found PM2.5 and CO positively associated with transmission of positive cases where as NO2 and SO2 have a negative relation after sensitive analysis.➢We have found temperature and wind speed have positive relation whereas, relative humidity and solar radiation have negative relation after sensitive analysis.➢Weather variables like relative humidity and solar radiation and rainfall are more sensitive than temperature and wind speed. Pollutants like NO2, PM2.5, PM10 and CO are more sensitive variables than SO2 in this study.

scholarly journals Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China

2021 ◽  
Author(s):  
yu luo ◽  
Peng Gao ◽  
Xingmin Mu
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
River Basin ◽  
Air Temperature ◽  
Hydrological Cycle ◽  
Meteorological Factors ◽  
Potential Evapotranspiration ◽  
Sensitivity Coefficients ◽  
The Impact

Potential evapotranspiration (ET) is an essential component of the hydrological cycle, and quantitative estimation of the influence of meteorological factors on ET can provide a scientific basis for studying the impact mechanisms of climate change. In the present research, the Penman-Monteith method was used to calculate ET. The Mann-Kendall statistical test with the inverse distance weighting were used to analyze the spatiotemporal characteristics of the sensitivity coefficients and contribution rates of meteorological factors to ET to identify the mechanisms underlying changing ET rates. The results showed that the average ET for the Yanhe River Basin, China from 1978–2017 was 935.92 mm. Save for a single location (Ganquan), ET increased over the study period. Generally, the sensitivity coefficients of air temperature (0.08), wind speed at 2 m (0.19), and solar radiation (0.42) were positive, while that of relative humidity was negative (-0.41), although significant spatiotemporal differences were observed. Increasing air temperature and solar radiation contributed 1.09% and 0.55% of the observed rising ET rates, respectively; whereas decreasing wind speed contributed -0.63%, and relative humidity accounted for -0.85%. Therefore, it was concluded that the decrease of relative humidity did not cause the observed ET increase in the basin. The predominant factor driving increasing ET was rising air temperatures, but this too varied significantly by location and time (intra- and interannually). Decreasing wind speed at Ganquan Station decreased ET by -9.16%, and was the primary factor underlying the observed, local “evaporation paradox.” Generally, increases in ET were driven by air temperature, wind speed and solar radiation, whereas decreases were derived from relative humidity.

Los Angeles County Evaluation System (LACES): Bringing Accountability to Alcohol and Drug Abuse Treatment through a Collaboration between Providers, Payers, and Researchers

2002 ◽  
Vol 32 (3) ◽  
pp. 865-879 ◽  
Author(s):  
Desirée Crèvecoeur ◽  
Beth Finnerty ◽  
Richard A. Rawson
Keyword(s):  
Substance Abuse ◽  
Los Angeles ◽  
Substance Abuse Treatment ◽  
Evaluation System ◽  
Los Angeles County ◽  
Large Scale ◽  
Addiction Severity Index ◽  
Data Set ◽  
Abuse Treatment ◽  
The Impact

The design and early implementation stages of a large-scale, system-wide evaluation of Los Angeles County's substance abuse treatment system (Los Angeles County Evaluation System: An Outcomes Reporting Program, or LACES) will be examined. In the first stage of implementation, individuals entering treatment will provide a standardized data set using the Los Angeles County Participant Reporting System (LACPRS) admission forms and the Addiction Severity Index (ASI). At treatment program discharge, the LACPRS discharge form will be administered, and at one-year post admission the ASI will be re-administered to a stratified sample of 1,500 individuals from 31 “sentinel programs.” Additional information will be gathered on the services provided by Los Angeles County alcohol and other drug treatment/recovery programs. Data from LACES will provide the treatment providers feedback on the impact of treatment services and will create the foundation of an ongoing evaluation system for the County's substance abuse treatment clients and services.

scholarly journals Coronavirus Disease 2019 (COVID-19) Infections Among Healthcare Workers, Los Angeles County, February–May 2020

2020 ◽  
Author(s):  
Stacy Hartmann ◽  
Zachary Rubin ◽  
Heidi Sato ◽  
Kelsey O Yong ◽  
Dawn Terashita ◽  
...  
Keyword(s):  
Los Angeles ◽  
Healthcare Workers ◽  
Los Angeles County ◽  
Infectious Period ◽  
Risk Of Infection ◽  
Term Care ◽  
Healthcare Facilities ◽  
Patient Interviews ◽  
The Impact

Abstract Across the world, healthcare workers (HCW) are at a greater risk of infection by coronavirus disease 2019 (COVID-19) due to the nature of their work. The Los Angeles County Department of Public Health (LAC DPH) set out to understand the impact of COVID-19 on healthcare facilities and HCWs by tracking and analyzing data from case-patient interviews of HCWs. As of 31 May, over 3 months into the pandemic, nearly 5500 positive HCWs were reported to LAC DPH, representing 9.6% of all cases. Cases reported working in 27 different setting types, including outpatient medical offices, correctional facilities, emergency medical services, and so forth, with the highest proportion from long-term care facilities (46.6%) and hospitals (27.7%). Case patients included both clinical and nonclinical roles, with nearly half (49.4%) of positive HCWs being nurses. Over two-thirds of HCWs (68.6%) worked at some point during their infectious period, and nearly half (47.9%) reported a known exposure to a positive patient and/or coworker within their facility. Overall, compared to all LAC cases, HCWs reported lower rates of hospitalization (5.3% vs 12.2%) and death (0.7% vs 4.3%) from COVID-19. There are many factors that increase HCWs risk of infection, including high-risk work environment, limited supply of personal protective equipment, and even pressure to help and work during a pandemic. In response to these data, LAC DPH created resources and provided guidance for healthcare facilities to best protect their patients and staff during the COVID-19 pandemic.

scholarly journals Exploring the Driving Factors of the Spatiotemporal Variation of Precipitation in the Jing–Jin–Ji Urban Agglomeration from 2000 to 2015

2020 ◽  
Vol 12 (18) ◽  
pp. 7426
Author(s):  
Feili Wei ◽  
Ze Liang ◽  
Yueyao Wang ◽  
Zhibin Huang ◽  
Huan Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Urban Climate ◽  
Explanatory Power ◽  
Interaction Factors ◽  
Impact Of Urbanization ◽  
Precipitation Changes ◽  
Annual Scale ◽  
The Impact

Urbanization has a significant impact on urban precipitation. Existing studies on precipitation pay more attention to the impact of natural and meteorological factors, and the research on the impact of urbanization on the spatial patterns of precipitation is still very deficient. Based on geographic detection, this study quantitatively analyzed the dominant, interaction, and sensitivity factors that affect precipitation changes in more than 150 urban units in Jing–Jin–Ji (Beijing–Tianjin–Hebei) during the process of urbanization. The research findings show the following: ① The dominant factors have seasonal differences in terms of the precipitation variation in Jing–Jin–Ji. The leading factors in summer were the change of radiation and relative humidity. The dominant factors in winter were the changes in radiation, relative humidity, and wind speed. On the annual scale, the dominant factors were the changes in relative humidity, aerosol optical depth, radiation, and wind speed. ② Whether in summer, in winter, or on the annual scale, urbanization can enhance the explanatory power of spatial variation of urban precipitation through interaction with natural/meteorological factors, and all the dominant interaction factors show a nonlinear enhancement trend. ③ The night light intensity and urban heat island can greatly amplify the explanatory power of other factors, thus becoming the most sensitive factor in urbanization precipitation changes. The above research can provide a theoretical basis for the formulation of urban climate policies and urban planning.

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