Stochastic Modelling of Air Pollution Impacts on Respiratory Infection Risk

The impact of air pollution on people’s health and daily activities in China has recently aroused much attention. By using stochastic differential equations, variation in a 6 year long time series of air quality index (AQI) data, gathered from air quality monitoring sites in Xi’an from 15 November 2010 to 14 November 2016 was studied. Every year the extent of air pollution shifts from being serious to not so serious due to alterations in heat production systems. The distribution of such changes can be predicted by a Bayesian approach and the Gibbs sampler algorithm. The intervals between changes in a sequence indicate when the air pollution becomes increasingly serious. Also, the inflow rate of pollutants during the main pollution periods each year has an increasing trend. This study used a stochastic SEIS model associated with the AQI to explore the impact of air pollution on respiratory infections. Good fits to both the AQI data and the numbers of influenza-like illness cases were obtained by stochastic numerical simulation of the model. Based on the model’s dynamics, the AQI time series and the daily number of respiratory infection cases under various government intervention measures and human protection strategies were forecasted. The AQI data in the last 15 months verified that government interventions on vehicles are effective in controlling air pollution, thus providing numerical support for policy formulation to address the haze crisis.     

Development of a dairy cattle drinking water quality index (DCWQI) based on fuzzy inference systems

The critical role of water quality for all animals, and especially dairy cattle, the dairy products of which are consumed by humans, raises the need for an index which represents well the quality of water consumed by dairy cattle. Noting the high subjectivity and inappropriate classifications that traditional methods apply to development an index, we aimed to develop a better index that measures the quality of drinking water supplied to dairy cattle (DCWQI¹) based on fuzzy logic. Using fuzzy logic enabled us to capture experts’ knowledge and to simulate the human's way of thinking in the design of the index. Our approach avoided the shortcomings of the previous models. We selected 20 parameters that available literature determined were critical to assessing the quality of water for dairy cattle to drink due mainly to their potential impacts both on dairy cattle and human health. These parameters were: dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, temperature, total dissolved solids (TDS), turbidity, fecal coliform, heterotrophic plate count, hardness, alkalinity, arsenic, lead, mercury, nickel, cadmium, chromium, total phosphorous, H₂S, nitrate, and fluoride. We used trapezoidal membership functions and the final ruleset consisted of 550 rules. Mamdani inference system captured experts’ knowledge and experience; center-of-gravity method was used to defuzzify the results. To evaluate the index performance, we conducted a case study of Karun River employing the water quality data from six sampling stations along the river over the period of 2007–2010 and compared the results to those from the National Sanitation Foundation (NSF) water quality index (WQI). Our study found that the water quality of Karun River lies in the low to medium range (annual mean index values of 38–55). In addition, the values from the fuzzy DCWQI were generally lower than the values from the NSF WQI, mainly because the DCWQI included heavy metals in its index, while the NSF WQI did not. Results of the present study suggest that DCWQI can be considered as a comprehensive tool for assessing the quality of water for dairy cattle drinking purposes and can be reliably used for that objective.     

Use of the water quality index and dissolved oxygen deficit as simple indicators of watersheds pollution

The use of the water quality index (WQI) and the dissolved oxygen deficit (D) as simple indicators of the watersheds pollution was investigated and compared in the Municipality of Las Rozas (north-west of Madrid, Spain). The quality of the water in Guadarrama and Manzanares rivers and Paris Park ponds, the main watersheds of this area was investigated during 2 years (from September 2001 to September 2003). It was found that the WQI was very useful for the classification of the waters monitored. The WQI was 70, which corresponds to “good” quality water at the sampling point 1 (entrance of Las Rozas) and decreased to around 64 (medium quality) at the sampling point 6 (outlet of Las Rozas) in the case of Guadarrama River. The WQI was around 65 in the influents of Manzanares River. Finally, in Paris Park the WQI ranged from around 72–55, which corresponded to a classification from “good” to “medium” quality, respectively. A high linear relationship between the WQI and the dissolved oxygen deficit (D) was found. Therefore, a fast determination of WQI may be carried out knowing the values of D, which are easily obtainable by field measurements. It was found an influence of the climate conditions on the values of WQI and D.     

Air quality and its response to satellite-derived urban form in the Yangtze River Delta,China

Air pollution is one of the top environmental concerns and causes of deaths and various diseases worldwide. An important question for sustainable development is to what extent urban design can improve or degrade urban air quality. In this article, we explored the relationship between ground-based observations of air pollution and urban form in the Yangtze River Delta (YRD), the largest metropolitan region in China. We analyzed six criteria pollutants (SO₂, NO₂, PM₁₀, PM_2.5, CO, O₃) and summarized metric (air quality index, AQI) from 129 ambient air quality monitoring stations during 2015. Urban form was characterized using six spatial metrics, including the size, shape, regularity, fragmentation and traffic coupling factor of urban patches, based on satellite-derived land cover data. The results indicated that: (1) PM_2.5, PM₁₀ and O₃ were three primary pollutants in the YRD. The annual average AQI was 79, and the air quality was “moderate” for human health, with the highest and lowest AQI appeared in winter (107) and summer (60). Moreover, the air quality of the southern areas (Zhejiang province, AQI: 68) was generally better than the northern parts (Jiangsu province, AQI: 86). (2) Through the size and shape of urban patches, urban form had a significant effect on urban air quality in the YRD. PARA_MN (Mean Perimeter-area ratio), ENN_MN (Mean Euclidean Nearest Neighbor Distance), CA (Total Urban Area) and NP (Number of urban patches) had the most significant impacts on air quality. PM₁₀ and PM_2.5 were two important pollutants highly positively related to CA and NP, while negatively related to PARA_MN and ENN_MN. In addition, the polycentric urban form was associated with high air quality. (3) Land use configuration was an important indicator to describe the urban air quality. When buffer distance of spatial scale was 25 km, air quality showed the highest correlation with forest coverage. A high forest coverage rate contributed to the better air quality, increasing or preserving the forested areas would help mitigate the air pollution.     

Using Social Media to Detect Outdoor Air Pollution and Monitor Air Quality Index (AQI): A Geo-Targeted Spatiotemporal Analysis Framework with Sina Weibo (Chinese Twitter)

Outdoor air pollution is a serious problem in many developing countries today. This study focuses on monitoring the dynamic changes of air quality effectively in large cities by analyzing the spatiotemporal trends in geo-targeted social media messages with comprehensive big data filtering procedures. We introduce a new social media analytic framework to (1) investigate the relationship between air pollution topics posted in Sina Weibo (Chinese Twitter) and the daily Air Quality Index (AQI) published by China’s Ministry of Environmental Protection; and (2) monitor the dynamics of air quality index by using social media messages. Correlation analysis was used to compare the connections between discussion trends in social media messages and the temporal changes in the AQI during 2012. We categorized relevant messages into three types, retweets, mobile app messages, and original individual messages finding that original individual messages had the highest correlation to the Air Quality Index. Based on this correlation analysis, individual messages were used to monitor the AQI in 2013. Our study indicates that the filtered social media messages are strongly correlated to the AQI and can be used to monitor the air quality dynamics to some extent.     

城市绿化对空气质量的影响研究——以中国27个省会城市为例

城市间绿化程度与空气污染比较及相关差异分析是提出城市环境管理措施的重要前提。选择全国27个主要省会城市,基于网络街景照片测定绿色指数差异,对比空气主要质量指标[空气质量指数(AQI)、细颗粒物(PM_2.5)、可吸入颗粒物(PM₁₀)、二氧化硫(SO_2)、二氧化氮(NO_2)、臭氧(O_3)、一氧化碳(CO)]的基础上,探讨了二者相关关系,旨在为提升环境质量、改善绿化水平提供基础数据。结果表明:(1)济南市和重庆市的绿色指数最高,分别达到是11.70%和11.55%,呼和浩特市和拉萨市城市绿色指数最低,在4%～5%。(2)海口市的空气质量最好,AQI为39.66,郑州市和济南市的空气质量最差,AQI年均值分别为117.34和113.93。但是不同空气指标城市排序间差异较大,比如PM_2.5、PM₁₀、NO_2以及SO_2年平均最低的城市都是海口,拉萨市年平均CO含量最低(0.55 mg·m^-3),哈尔滨市年平均O_3含量最低(77.08μg·m^-3)。(3)相关关系分析发现,增加城市的绿色程度,伴随着空气质量的改善,如沈阳、南宁、合肥等城市;但对于某些城市,则存在明显的正相关,如兰州、昆明、贵阳等城市,这意味着城市越绿伴随着空气污染的加重。尽管大量研究已经表明,城市绿色植被能够起到滞尘降低污染的作用,目前我国主要城市的空气污染程度,仅依靠城市绿化改善已经远远不够,甚至某一些城市绿色植被存在阻碍空气流通的作用。上述研究结果为科学规划城市绿化、提升空气质量提供基础数据支撑。     

Comparative study of ecological indices for assessing human-induced disturbance in coastal wetlands of the Laurentian Great Lakes

Several ecological indices have been developed to evaluate the wetland quality in the Laurentian Great Lakes. One index, the water quality index (WQI) can be widely applied to wetlands and produces accurate measurements of wetland condition. The WQI measures the degree of water quality degradation as a result of nutrient enrichment and road runoff. The wetland fish index (WFI), wetland zooplankton index (WZI), and the wetland macrophyte index (WMI), are all derived from the statistical relationships of biotic communities along a gradient of deteriorating water quality. Compared to the WQI, these indices are less labor-intensive, cost less, and have the potential to produce immediate results. We tested the relative sensitivity of each biotic index for 32 Great Lakes wetlands relative to the WQI and to each other. The WMI (r² = 0.84) and WFI (r² = 0.75) had significant positive relationships (P < 0.0001) with the WQI in a linear and polynomial fashion. Slopes of the WMI and WFI were similar when comparing the polynomial regressions (ANCOVA; P = 0.117) but intercepts were significantly different (P = 0.004). The WZI had a positive relationship with the WQI in degraded wetlands and a negative relationship in minimally impacted wetlands. The strengths and weaknesses of each index can be explained by the interactions among fish, zooplankton, aquatic plants and water chemistry. The distribution of different species indicative of low and high quality in each index provides insight into the relative wetland community composition in different parts of the Great Lakes and helps to explain the differences in index scores when different organisms are used. Our findings suggest that the WMI and WFI produce comparable results but the WZI should not be used in the minimally impacted wetlands without further study.     

Comment on “Assessing water quality of five typical reservoirs in lower reaches of Yellow River,China: Using a water quality index method” by Wei Hou,Shaohua Sun,Mingquan Wang,Xiang Li,Nuo Zhang,Xiaodong Xin,Li Sun,Wei Li,and Ruibao Jia (2016) [Ecological Indicators, 61, 309–316]

Hou et al. (2016) recently developed a water quality index (WQI) for assessing water quality of five typical reservoirs. Despite all the merits of the practical WQI, it suffers from lack of uncertainty consideration; a fact that motivated the present discussion focusing on mitigation of uncertainty in water quality assessment. In this regard, superiority of employing fuzzy WQI (FWQI) rather than crisp WQI is emphasized. Due to robustness of FWQI in handling uncertainties surrounding data acquisition, employment of fuzzy concept can improve water quality assessment and monitoring to generate results which are more consistent with real world conditions.     

River quality analysis using fuzzy water quality index: Ribeira do Iguape river watershed, Brazil

Intrinsic uncertainties and subjectivities of environmental problems have been increasingly dealt by using computation methods based on artificial intelligence. In order to evaluate this tool's applicability, this study proposed the creation of a new water quality index based on fuzzy logic, the fuzzy water quality index (FWQI). The performance of the index proposed in the present work is assessed through a comparison with several water quality indices (WQIs) suggested in the literature, using data from hydrographic surveys of the Ribeira de Iguape River, in the southwestern part do São Paulo State, Brazil, from 2004 to 2006. The index was reasonably close to the other indices and showed a good correlation with the WQI traditionally calculated in Brazil. This new index may also be used as an alternative tool for decision-making in environmental management.     

两类水质综合评价方法的特点及其在河流水环境管理中的作用

河流水质恶化已成为严峻的环境问题,针对河流开展水质综合评价对河流水环境管理具有重要意义。依据不同的评价目标,选择合理的综合评价的评估标准成为河流水质评价中重要的问题之一。通过基于期望值和阈值的水质健康综合评估法(ETI)和水质质量指数(WQI)两种水质综合评价方法对"引滦入津"工程重要水源地伊逊河水质进行评价,结果显示:1)伊逊河水质ETI评估结果为良好等级,WQI评估结果为一般等级;2)伊逊河采样点水质在ETI评价中分布于4个等级,WQI仅分布于2个等级;3)伊逊河自上游至下游水质显著降低,特别是ETI评估中由优秀等级转变为差等级;4)ETI和WQI评估结果显示极显著相关性(R=0.951,P0.01);5)通过逐步多元回归,ETI评估结果的主导因子是DO、EC、SS、BOD_5和TP(P0.05),WQI评估结果的主导因子是DO、SS、BOD_5、TP、TN和NH_3-N(P0.05)。进一步分析表明伊逊河水质恶化与其流域内土地利用情况密切相关。在河流水环境管理中,ETI作为一种水质相对值评估方法,能更好体现流域内水质差异的区分度,便于管理者迅速定位流域内亟需治理的河流或河段,同时能够根据河流自身特征制定管理目标,可作为河流管理绩效评估的有效手段;而WQI作为一种水质绝对值评估方法,更适用于河流水质时间变化评估,对河流经长期治理后的管理效果评价起到重要作用。     

辽河铁岭段水生生物和微生物状况、评价及变化趋势

以2003年至2007年辽河铁岭段水生生物浮游植物、微生物监测数据为基础,应用Shannon-Wiener多样性指数法和Kolkwitz-Marsson污水生物系统法等生物学指标分析方法,分析辽河铁岭段水质状况,评价水质质量,划分水质污染等级,得出水质变化趋势。     

Application of modified water quality indices as indicators to assess the spatial and temporal trends of water quality in the Dongjiang River

The Dongjiang River plays an important role in southern China, as a source for irrigation and potable water of Hongkong and the other parts of the Pearl River Delta (PRD). The water quality index (WQI) was calculated to assess the spatial and temporal variability and identify the classification of water quality in the river. In order to simplify the procedure and reduce the analytical costs of the water quality evaluation, a modified WQI (defined as WQI_min) was introduced based on Principal Component Analysis (PCA) and correlations analyses of the water parameters detected in dry and wet seasons during 2011–2012. Compared with the previous index, similar spatial changing trend and classification of the water quality were obtained by WQI_min, which was composed of pH, temperature, total suspended solid, NH₄⁺-N, and NO₃⁻-N. The results showed an excellent water quality in the tributary site near the reservoir, a good water quality in the upstream of the river, and medium water quality in the downstream of the river, which suggested that the urban wastewater originated from increasing population size and industry development in the downstream mainly led to the deterioration of water quality along the river. Moreover, WQI_min could more adequately reflect the seasonal changes of water quality which was slightly worse in dry season than wet season. Our results also suggest that continuous monitoring should be conducted to prevent pollution from industry and anthropogenic activities.     

A dynamic water quality index model based on functional data analysis

A water quality index (WQI) incorporates two shortcomings in the dynamic assessment of water quality, namely: (1) the sampling time series must be identical for each indicator and no missing data should occur, and (2) stationary weights cannot represent the changes in the pollutant importance. To solve these problems, the present study introduces the functional data analysis method into WQI research and establishes a dynamic WQI (D-WQI) model. D-WQI is a generalization of the conventional WQI. In the D-WQI model, the changes of water quality and pollutant importance are represented in the form of dynamic functional curves. The generation methods of the concentration curves, sub-index curves, dynamic weight curves, and WQI curves are discussed. As an illustration, the D-WQI model is applied in the water quality assessment of the Changjiang River in Sanjiangying in 2012. Result shows that the river can be classified as II (good) throughout the year, which can satisfy the requirement of the Chinese South-to-North Water Diversion Project.     

Assessing environmental conditions of the Río Champotón (México) using diverse indices and biomarkers in the fish Astyanax aeneus (Günther, 1860)

The upper reaches of the Río Champotón in southwestern Mexico are largely unknown to science. This river is sustaining anthropogenic and natural disturbances by deforestation, substitution of natural vegetation, and agricultural non-point source pollution. Furthermore, hurricanes provoke flooding of these areas, exposing the aquatic biota to pollutants. Several approaches were used to assess the effects of environmental conditions along the freshwater portion of the Río Champotón in the native fish Astyanax aeneus and to determine the feasibility of extrapolating responses between levels of organization, and to link spatial and seasonal fish responses with water quality features. A water quality index (WQI) was employed as an indicator of environmental conditions, a set of sub-organismal biomarkers in A. aeneus (lipid peroxidation, glutathione S-transferase, ethoxyresorufin-O-deethylase and lactate dehydrogenase) was monitored to determine the integrated biomarker response (IBR), and somatic indices (individual and population-level responses; gonadosomatic, hepatosomatic, and condition factor) were characterized. Canonical correspondence analysis revealed spatial and seasonal variations in all the approaches. The dry and the wet season (April and July) exhibited low WQI scores, but decreased hardness and conductivity values as well as increased WQI values occurred during the post-hurricane season when crop fields adjacent to the riverbed were inundated. Although the WQI suggested low levels of contamination, the biomarkers did not remain static; wide fluctuations reflected fish exposure to seasonal stressors that provoked impairments in biological responses. All biomarker values were high, mainly in the post-hurricane season (November). The IBR exhibited two peaks that may be related to stress periods (April and November), with decreasing post-stress values suggesting the existence of homeostatic processes in the fish. The somatic indices reflected some seasonal and spatial differences at the population level – the gonadosomatic and hepatosomatic indices peaked in the middle of July and November, respectively – but the condition factor remained constant among sites and study periods. Future studies are needed to explore the link between the fish biomarker responses and possible causal stressors (natural or anthropogenic).     

A Support Tool for Air Pollution Health Risk Management in Emerging Countries: A Case in Brazil

This study evaluated the health risk due to air pollution in São Paulo State, Brazil, comparing Brazil's standards and the World Health Organization (WHO) air quality guidelines. New methodology was applied considering, as hazard parameters, the maximum average concentrations of SO₂, NO₂, O₃, CO, and PM₁₀ from 2007 to 2011. As exposure parameters, population density and location sensitivity were considered. A health risk index based on fuzzy logic was performed to integrate the selected parameters, giving the likelihood of reaching different risk levels. Data gathering and spatial representation of parameters and risk scores were performed by means of geographic information system (GIS). Highest values of risk were observed in the Metropolitan Area of São Paulo and near the cities of Cubatão and Piracicaba, associated to vehicular and industrial emissions and sugar cane burning. Discussions about the need to revise national air quality standards have intensified over the past years. Generally, indices used for air quality do not consider the population exposed. This study showed the integration of GIS with fuzzy logic methodology to be a useful tool for health risk assessment. When evaluating the risk, exposure parameters must be intrinsically considered to enhance the importance of population vulnerability when assessing environmental problems, especially in emerging economies.     

Predicting Water Quality Index (WQI) by feature selection and machine learning: A case study of An Kim Hai irrigation system

A variety of water quality indices have been used to assess the state of waterbodies all over the world. In calculating a Water Quality Index (WQI), traditional methods require the evaluation of many water quality parameters, making them costly and time-consuming. In recent years, machine learning (ML) algorithms have emerged as an effective tool to solve many environmental problems, including water quality management. In this study, we investigate the performance of the ML-based method in calculating the WQI. We apply several feature selection techniques to select the key parameters fed the ML models. Experiments are carried out to evaluate the WQI based on a dataset collected from 2007 to 2020 of An Kim Hai system, one of the most important irrigation systems in the north of Vietnam. The obtained results show that the application of selection methods allows reducing significantly the number of water quality parameters fed the ML models without losing their accuracy. In particular, by using the embedded method, we find out four important parameters, including Coliform, DO, Turbidity, and TSS, that have the greatest impact on water quality. Based on these parameters, the Random Forest model provides the best accuracy in predicting the WQI values from the An Kim Hai system with a Similarity of 0.94. The combination of feature selection and ML methods is then considered an effective alternative for calculating the WQI, leading to a desirable performance and a reduction of input parameters. This makes water quality monitoring less costly, substantial effort, and time.     

Assessing water quality and classifying trophic status for scientifically based managing the water resources of the Lake Timsah,the lake with salinity stratification along the Suez Canal

Lake Timsah is considered as the biggest water body at Ismailia City with a surface area of 14?km². It is a saline shallow water basin lies approximately mid-way between the south city of Suez and the north city of Port Said at 30^o35′46.55“N and 32^o19′30.54″E. Because it receives water with high and low salinities, salinity stratification is producing in the Lake Timsah, with values of 14–40‰ for the surface water and over 40‰ for the bottom water. The temperature of the lake water decreased to below 19 °C in the winter and rose to above 29?°C in the summer; the concentration of dissolved oxygen ranged between 6.5 and 12.2?l^?1 and the pH fluctuated between 7.9 in its lower value and 8.2 in its higher value. Water transparency was very low as indicated by Secchi disc readings recorded during this study and varied between 0.3 and 2.7?m. The main chemical nutrient (phosphorus) reached its highest levels of 96?µg?l^?1 in winter and their lowest values of 24?µg?l^?1 during summer. This nutrient concentration is high especially by comparing with those of unpolluted marine waters, but is typical of the more eutrophic coastal waters worldwide. The composition and abundance of phytoplankton with dominancy of diatoms and increased population density (20,986 cell l^?1) reflect the eutrophic condition of the lake. The intensive growth of phytoplankton was enriched by high concentration of chlorophyll a with annual values ranged between 6.5 and 56?µg?l^?1. The objective of the present work was quantitative assessment of the quality of the water of the Lake Timsah using different approaches. During the present study, three different approaches were applied for the quantitative assessment of Lake Timsah water quality: the trophic state index (TST); trophic level index (TLI) and water quality index (WQI). Application of the trophic state and trophic level indices (TSI & TLI) revealed that the Lake Timsah has trophic indices of 60 and 5.2 for TSI and TLI, respectively. Both indices reflected the eutrophic condition of the lake waters and confirmed that the eutrophication is a major threat in the Lake Timsah. On the other hand, the WQI calculated for the Lake Timsah during the present study with an average of 49 demonstrated that the water of the Lake Timsah is bad and unsuitable for main and/or several uses. Moreover, WQI allows accounting for several water resource uses and can serve a more robust than TSI and/or TLI and can be used effectively as a comprehensive tool for water quality quantification. In conclusion, the three subjective indices used for the assessment process for the lake water are more suitable and effective for needs of the sustainable water resources protection and management of the Lake Timsah.     

Aggregated indices for trends in eutrophication of different types of fresh water in the Netherlands

As in many European countries, eutrophication of surface waters is a key problem in the Netherlands, caused by high concentrations of nitrogen (N) and phosphorus (P). For implementation of the EU Water Framework Directive (WFD) in the Netherlands, surface water types have been identified and for each water type environmental quality standard concentrations (EQS) were determined for both nutrients. With these standards, a new method was developed to quantify trends in water quality with respect to eutrophication on a national scale for the period 1990–2010. Firstly, monitoring data were aggregated to seven classes of surface water. Next, for each water class and each nutrient, a Nutrient Index was developed to express the degree of exceedance of EQS (distance-to-target method). The Nutrient Index for N shows a minor exceedance of EQS as well as an improvement in all water types; for P, the index shows a major exceedance in small stagnant waters throughout the 1990–2010 period, whereas in major rivers and lakes levels were close to EQS. Thirdly, the indices for seven surface water classes were aggregated to one index for each nutrient, and finally to a national Eutrophication Index combining N and P. This Eutrophication Index integrates all monitoring data and shows an improvement in the period 1990–2003 and stabilization since 2004.     

茅尾海营养状况及其来源研究

根据2008年茅尾海海域水质调查结果,采用营养指数法、营养状态质量指数法、有机污染指数法对海湾营养状况进行了评价,并结合2007年入海污染源调查结果,探讨了茅尾海的营养盐来源。结果表明:茅尾海受无机氮污染较重,海域处于呈富营养状态,有机污染程度属2级,表明开始受到有机污染。营养盐高值区集中在北部海域和东部海域。钦江、茅岭江等入海河流携带入海的营养盐是海域营养的主要来源,占入海污染物总量的79%以上,其次来自钦州湾外海的混合排污口。2001~2007年茅尾海DIN、DIP年均浓度分别与入海河流氮、磷营养盐入海负荷呈显著正相关,两者的相关系数分别为0.873和0.824。     

Using Auchenorrhyncha (Insecta: Hemiptera) to develop a new insect index in measuring North American tallgrass prairie quality

Auchenorrhyncha (i.e., leafhoppers, treehoppers, spittlebugs, and planthoppers) represent some of the most diverse groups of herbivorous insects in the tallgrass prairie biome, they have close associations with many native prairie grasses and forbs, and respond in predictable ways to changes in native grassland degradation. These attributes make Auchenorrhyncha ideal candidates in the development of a habitat quality index to measure tallgrass prairie quality. In this study we propose the development of a species-based habitat quality index called the Auchenorrhyncha quality index or AQI as a useful method in tracking the condition of tallgrass prairie quality. The AQI is computed by summing six ecological characteristics (i.e., host plant specificity, voltinism, overwintering microhabitat, wing length, habitat fidelity, and origin) for each Auchenorrhynchan insect encountered, yielding coefficient of conservatism (CC) values that range from 0 (habitat generalist/tolerant to disturbance) to 18 (habitat specific/intolerant to disturbance). These CC values are averaged and combined with species richness producing un-weighted by abundance AQI (AQI_w/outN) and weighted by abundance AQI (AQI_w/N). The performance of the AQI was evaluated by examining the effects of sampling intensity on this index using a sweep net and a vacuum apparatus from 10 sites located on the three main North American tallgrass prairie communities, wet-mesic, sand, and loess hill. Scientists and land managers can adequately sample Auchenorrhyncha from four transects using a vacuum. Also, the highest AQI values were found from loess hill and sand prairies, indicating that conservation efforts should focus on these prairie communities. Additional applications of the AQI may include: (1) discriminating prairie quality at various spatial scales; (2) testing hypotheses about the effects of disturbance on prairie habitat (e.g. prescribed burning); (3) using the AQI as a model in developing habitat quality indices based on other diverse groups of grassland insects; and (4) the AQI has the capacity to be readily modified in assessing the quality of other biomes. Ultimately, the AQI should be used in combination with other habitat quality indices based on other diverse groups of organisms, such as plants and other insects, to provide a more complete assessment of native habitat quality.