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合肥城区空气微生物分布特征初步研究 总被引:3,自引:0,他引:3
目的 了解合肥市空气微生物群落结构和分布特征.方法 采用自然沉降法对合肥市10个采样点进行空气微生物监测.结果 合肥城区空气细菌总数均值为5.04×104cfu/m3,真菌总数均值为3.75×103cfu/m3,合计为5.41×104cfu/m3,其中真菌占6.92%;对不同采样点的空气细菌和真菌进行了初步鉴定,优势细菌属为微球菌属(Micrococcus)、芽孢杆菌属(Bacillus)、假单胞菌属(Pseudomonas)和葡萄球菌属(Staphylococcus),分别占总细菌的37.3%,21.0%,10.3%,9.5%;真菌共检出5属,其中优势菌属为曲霉属(Aspergillum)、青霉属(Penicillium)、毛霉属(Mucor)、根霉属(Rhizopus),分别占总真菌的42.0%,36.0%,4.4%,4.2%.结论 合肥城区空气微生物的分布不仅与环境因素、污染因子有关,还受到人类活动、动植物等因素的影响. 相似文献
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了解雾霾对校园环境空气中微生物分布的影响,为校园环境卫生治理、疾病防控及个人防护提供实验数据.方法 抽取河北大学医学部室外(医苑广场、医苑路、操场)和室内(食堂、教室、学生宿舍)等6个人员主要活动场所,在无雾霾、轻度雾霾、中度雾霾及重度雾霾天气状况下按自然沉降法分别采样,测定空气中细菌和霉菌菌落总数.结果 中度和重度雾霾天气时室外空气中细菌、霉菌菌落总数均高于室内(P值均<0.05). 室外各采样点细菌、霉菌总数均随雾霾程度加重而升高,其中医苑路升高最为显著(P值均<0.05);室内各采样点除学生宿舍内细菌、霉菌浓度在重度雾霾天高于其他天气状况外,食堂、教室随天气变化不明显(P值均>0.05).结论 雾霾程度加重时校园室外空气中细菌、霉菌菌落总数均随之升高,而室内空气微生物浓度与雾霾程度关联较小. 相似文献
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九江市空气微生物动态变化监测 总被引:2,自引:0,他引:2
目的 了解九江市空气微生物动态变化特征。方法于1998年7月(夏)和10月(秋)、1999年1月(冬)和4月(春)选择九江市有代表性的工业区、郊区、文化商业居住区、居民区、工业商业交通混合区、火车站地区进行空气微生物监测。结果 九江市不同功能分区空气微生物菌落数分别为工业商业交通混合区:1948cfu/m^3;工业区:1613cfu/m^3;文化商业居住区:1434cfu/m^3;居民区:1358cfu/m^3;火车站:1170cfu/m^3;郊区:552cfu/m^3。季节变化为夏季>秋季>春季>冬季。每日不同时段的变化为11:00>15:00>23:00>7:00>19:00>3:00。结论 九江市空气微生物菌落数的高峰出现在工业商业交通混合区的夏季,每日只出现1次高峰。 相似文献
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了解高校空气中微生物污染情况,为控制微生物污染和改善室内空气品质提供参考.方法 于2018年11月,采用安德森六级采样器对北京市某高校不同功能区、不同时态的空气微生物进行采样并培养.结果 在测试期间,不同功能区、不同时态的微生物气溶胶体积浓度差异有统计学意义(F值分别为3.99,7.77,P值均<0.05),卫生间的细菌体积浓度相对较高,平均体积浓度为659 CFU/m3,校园的真菌体积浓度相对较高,平均体积浓度为660 CFU/m3;12:00的空气中细菌和真菌体积浓度相对较低,平均体积浓度分别为320和322 CFU/m3.空气中微生物的粒径分布特征呈现大致相同的规律,微生物体积浓度峰值集中出现在Ⅳ,V级(1.1~3.3 μm),其中细菌最高体积浓度值为253 CFU/m3,出现在V级;真菌最高体积浓度值为249 CFU/m3,出现在Ⅳ级.结论 高校空气中的微生物体积浓度主要集中在1.1~3.3 μm粒径大小.需采取有针对性的综合防治措施来改善空气品质. 相似文献
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医院是病人和健康人群交流频繁的场所,各医疗环境进行空气细菌监测,对于预防院内呼吸道传染病的传播有很重要的意义.为此,我们对各医疗环境进行室内空气细菌培养及主要菌群的鉴定,现报道如下. 相似文献
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黄山森林景区空气微生物含量及变化规律 总被引:6,自引:0,他引:6
目的了解黄山森林景区空气微生物含量及其分布特点。方法于2002年4月-2003年1月在黄山森林景区选取19个测定点,在合H巴市区选取4个测定点,采用自然沉降法进行空气细菌和真菌含量监测,并测定空气负离子浓度。结果夏季黄山森林景区空气细菌总数均值为(503±461)cfu/m^3,空气真菌总数均值为(550±233)cfu/m^3。夏季空气中细菌总数与海拔高度呈负相关,相关系数为-0.580(P〈0.05);真菌总数与海拔高度呈负相关,相关系数为-0.345(P〈0.05)。黄山森林景区一年中大气微生物含量夏季最高,冬季最低。夏季温泉景区大气细菌和真菌的沉降量一日内有2个高峰,细菌在10:00和22:00左右,真菌在7:00和19:00左右,并均在13:00呈现一个低谷。空气细菌总数与负离子数成负相关,相关系数为-0.484(P〈0.05);真菌总数与负离子数呈负相关,相关系数为-0.855(P〈0.05)。合肥市区各测定点卒气细菌总数和微生物总数均高于黄山森林景区(除西大门外)。结论黄山森林景区空气微生物含量属清洁级。空气细菌和真菌总数随负离子数增多和海拔高度增加而减少。 相似文献
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化妆品生产车间空气细菌的分布特征 总被引:1,自引:0,他引:1
目的 探索各类化妆品生产车间空气细菌的分布与变化情况,对化妆品生产企业的良好作业规范(GMP)建立提供科学依据.方法 于2001年10月-2003年12月选择广州市24家不生产粉类(如膜粉、胭脂、粉饼等)的化妆品厂,其中采用空气过滤净化系统的化妆品厂(以下简称净化厂)11家,未采用空气过滤净化系统的化妆品厂(以下简称非净化厂)13家,以及未采用空气过滤净化系统生产粉类(面膜、胭脂、眼影等)的化妆品厂(以下简称粉类厂)9家.分别在净化厂和非净化厂的生产车间(更衣间、卫生通道、配料间、制作间、静置间、储瓶间、分装间)和粉类厂的生产车间(更衣间、卫生通道、配料间、制作间、静置间、打粉间、压粉间、分装间)设采样点,在室外设对照点.在生产前(紫外线消毒30min后,未采用空气过滤净化系统的化妆品厂不开通风排气装置)、生产后(生产1 h)各采样1次,分别在冬春(11-3月)、夏秋(4-10月)用撞击法重复采样,测定空气细菌总数.结果 3类化妆品厂生产车间空气细菌总数分别是净化厂<非净化厂<粉类厂,差异有统计学意义;生产车间生产中空气细菌总数的P25~P25为160~1 130 cfu/m^3;净化厂和粉类厂生产车间空气细菌总数季节差异无统计学意义,非净化厂生产车间空气细菌总数夏秋季>冬春季,差异有统计学意义;生产时净化厂以更衣间、静置间和分装间空气细菌总数相对较高,非净化厂以卫生通道、更衣间和分装间空气细菌总数相对较高;粉类厂更衣间、卫生通道、制作间空气细菌总数相对较高.非净化厂和粉类厂细菌总数随外界变化而变化;车间空气中0.5 μm尘埃粒子数与空气细菌总数呈正相关,有统计学意义(rs=0.358,P<0.000 1).结论 应该重视化妆品生产厂更衣间、卫生通道、静置间和分装间消毒和通风,加强产品生产过程的密封,提高生产的机械化、自动化,增加除尘设备的投入.选址需防止外界污染物对生产车间的影响. 相似文献
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随着医院就医环境的改善,越来越多的医院使用中央空调,而空调室内的空气质量也就成为医务人员及病人担忧的问题。为此,我们对某医院中央空调手术室进行了空气微生物学监测及动态观察。1内容与方法(1)采样点、采样次数及时间:选取每间手术间手术床边、空调送风口、空调排风口各为1个采样点,每日每点采样3次,时间为手术前消毒 相似文献
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医院空气微生物分布调查 总被引:1,自引:1,他引:0
目的了解医院各科室空气中的微生物分布,以便更好地指导临床消毒工作,预防医院感染。方法选择上午门诊高峰期,采用平板沉降法采集医院不同区域的空气样本,普通琼脂平板培养后计数菌落,革兰染色及分离鉴定,根据菌落的溶血情况、革兰染色进行判断分析;选择非医院环境的超市和学院图书馆进行对照。结果医院门诊大厅细菌总数超出国家标准的37.0%,内科病房超出国家标准15.0%;其他科室和非医院环境的空气细菌数符合国家标准,门诊大厅和病房主要以革兰阳性球菌为主。结论医院空气中的微生物多于普通公共场所,特别是人流密集的门诊区域细菌含量更高;医院有存在着发生医院感染的隐患。 相似文献
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目的 调查小学饮水机水中铜绿假单胞菌污染情况。方法 2015年10月采集徐州市市区5所小学50个饮水机内的水样50份,按照《2015年江苏省食品微生物及其致病因子监测工作手册》中《铜绿假单胞菌检验标准操作程序》进行检测。结果 铜绿假单胞菌检出率为24.00%(12/50);定量结果最高为510 cfu/mL,最低为1 cfu/mL;合格率为76.00%(38/50)。同一学校不同饮水机检出的2株铜绿假单胞菌的相似系数为100.00%,距离相近学校检出的铜绿假单胞菌相似系数达60.00%~80.00%;距离相对较远的学校饮水机中检出的铜绿假单胞菌相似系数为60.00%;不同学校检出的铜绿假单胞菌比同一所学校不同饮水机水检出的铜绿假单胞菌相似系数高;不同学校检出的铜绿假单胞菌的同源性比同一所学校不同的桶装水检出的铜绿假单胞菌相似系数高。结论 小学饮水机桶装水中有铜绿假单胞菌污染,应提高中小学生桶装水卫生知识,并定期对饮水机进行消毒,建立监管制度,防止校园内学生腹泻疾病的发生。 相似文献
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Henry V. Doctor Alabi Olatunji Abdul’Azeez Jumare 《Online Journal of Public Health Informatics》2012,4(3)
Maternal and child health indicators are generally poor in Nigeria with the northern part of the country having the worst indicators than the southern part. Efforts to address maternal and health challenges in Nigeria include, among others, improvement in health and management information systems. We report on the experience of mobile phone technology in supporting the activities of a health and demographic surveillance system in northern Nigeria. Our experience calls for the need for the Nigerian Government, the mobile network companies, and the international community at large to consolidate their efforts in addressing the mobile network coverage and power supply challenges in order to create an enabling environment for socio-economic development particularly in rural and disadvantaged areas. Unless power and mobile network challenges are addressed, health interventions that rely on mobile phone technology will not have a significant impact in improving maternal and child health. 相似文献
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Brian E. Dixon Roland E. Gamache Shaun J. Grannis 《Online Journal of Public Health Informatics》2013,5(1)
Objective
To characterize state and local health agency relationships with health information exchange organizations.Introduction
There is growing interest in leveraging available health information exchange (HIE) infrastructures to improve public health surveillance (1). The Health Information Technology for Clinical and Economic Health Act and Meaningful Use criteria for electronic health record (EHR) systems are among the factors driving the development, adoption and use of HIEs. HIEs deliver or make accessible clinical and administrative data as patients are admitted, discharged, and transferred across hospitals, clinics, medical centers, counties, states and regions (2). While several HIE infrastructures exist (3), there is little evidence on the engagement in HIE initiatives by state and local health agencies.Methods
An online survey of state and local health officials was conducted in six states where HIEs were known to be present. Half of the states were funded by the Centers for Disease Control and Prevention (CDC) to engage public health agencies in HIE activities; the other half received no such funding. A total of 143 officials were invited to participate; 73 (51%) responded. The survey asked respondents about their agencies awareness, engagement, and data exchange with HIEs. The survey further asked agencies about their perceptions of barriers and challenges to public health engagement with HIE organizations.Results
Just 25% of agencies had a formal relationship, typically created through a memorandum of understanding or data usage agreement, with at least one nearby HIE. The majority (54%) of agencies either had no relationship (20%) or only an informal relationship (34%) with an HIE. The remaining agencies (18%) reported that no HIE existed in their jurisdiction. Agencies in states that had received CDC funding for HIE engagement were more likely (14 versus 2) to be formally partnered with an HIE.Conclusions
Few public health agencies are formally engaged in HIE. Financial costs, human resources, and concerns regarding privacy/security were the top cited barriers to broader engagement in HIE. For public health to be an active participant in and reap the benefits of HIE, greater investment in state and local public health informatics capacity, including human resources, and education regarding HIE privacy and security practices are needed. 相似文献16.
Jennifer Stoll Richard Quartarone Miguel Torres-Urquidy 《Online Journal of Public Health Informatics》2013,5(1)
Objective
Recent scholarship has focused on using social media (e.g., Twitter, Facebook) as a secondary data stream for disease event detection. However, reported implementations such as (4) underscore where the real value may lie in using social media for surveillance. We provide a framework to illuminate uses of social media beyond passive observation, and towards improving active responses to public health threats.Introduction
User-generated content enabled by social media tools provide a stream of data that augment surveillance data. Current use of social media data focuses on identification of disease events. However, once identification occurs, the leveraging of social media in monitoring disease events remains unclear (2, 3). To clarify this, we constructed a framework mapped to the surveillance cycle, to understand how social media can improve public health actions.Methods
This framework builds on extant literature on surveillance and social media found in PubMed, Science Direct, and Web of Science, using keywords: “public health”, “surveillance”, “outbreak”, and “social media”. We excluded articles on online tools that were not interactive e.g., aggregated web-search results. Of 2,064 articles, 23 articles were specifically on the use of social media in surveillance work. Our review yielded five categories of social media use within the surveillance cycle (3. Monitor response to the information (passive)
5. Monitor response to intervention (passive)