Variability of the treated biomedical waste disposal behaviours during the COVID lockdowns.

Literature review suggests that studies on biomedical waste generation and disposal behaviors in North America are limited. Given the infectious nature of the materials, effective biomedical waste management is vital to the public health and safety of the residents. This study explicitly examines seasonal variations of treated biomedical waste (TBMW) disposal rates in the City of Regina, Canada, from 2013 to 2022. Immediately before the onset of COVID-19, the City exhibited a steady pattern of TBMW disposal rate at about 6.6 kg∙capita-1∙year-1. However, the COVID-19 pandemic and its associated lockdowns brought about an abrupt and persistent decline in TBMW disposal rates. Inconsistent fluctuations in both magnitude and variability of the monthly TBMW load weights were also observed. The TBMW load weight became particularly variable in 2020, with an interquartile range 4 times higher than 2019. The average TBMW load weight was also the lowest (5.1 tonnes∙month-1∙truckload-1) in 2020, possibly due to an overall decline in non-COVID-19 medical emergencies, cancellation of elective surgeries, and availability of telehealth options to residents. In general, the TBMW disposal rates peaked during the summer and fall seasons. The day-to-day TBMW disposal contribution patterns between the pre-pandemic and post-pandemic are similar, with 97.5% of total TBMW being disposed of on fixed days. Results from this Canadian case study indicate that there were observable temporal changes in TBMW disposal behaviors during and after the COVID-19 lockdowns.

A multi-jurisdictional study on the quantification of COVID-19 household plastic waste in six Latin American countries.

This study examines urban plastic waste generation using a citizen science approach in six Latin American countries during a global pandemic. The objectives are to quantify generation rates of masks, gloves, face shields, and plastic bags in urban households using online survey and perform a systematic cross-jurisdiction comparisons in these Latin American countries. The per capita total mask generation rates ranged from 0.179 to 0.915 mask cap-1 day-1. A negative correlation between the use of gloves and masks is observed. Using the average values, the approximate proportion of masks, gloves, shields, and single-use plastic bags was 34:5:1:84. We found that most studies overestimated face mask disposal rate in Latin America due to the simplifying assumptions on the number of masks discarded per person, masking prevalence rate, and average mask weight. Unlike other studies, end-of-life PPE quantities were directly counted and reported by the survey participants. Both of the conventional weight-based estimates and the proposed participatory survey are recommended in quantifying COVID waste. Participant' perception based on the Likert scale is generally consistent with the waste amount generated. Waste policy and regulation appear to be important in daily waste generation rate. The results highlight the importance of using measured data in waste estimates.

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level.

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site-disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.

Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices.

Illegal dump sites (IDS) pose significant risks to human and the environment and are a pressing issue worldwide. Due to their secretive nature, the detection of IDS is costly and ineffective. In this study, an analytical framework was developed to detect probable IDSs in rural and remote areas using nighttime light (NTL) as a proxy for populated areas. An IDS probability map is produced by aggregation of Landsat-8 and Suomi NPP satellite imagery, multiple-criteria decision-making analysis, and classification tools. Six variables are considered, including modified soil adjusted index, land surface temperature, NTL, highway length, railway length, and the number of landfills. Vulnerability of the inhabitants on reserve lands was assessed using three sample regions. The method appears effective in reducing potential IDSs. Only about 7% of the 31,285 km2 study area are identified as probable IDS, being classified as "very high" and "high". Landfills without permit are found more effective in lowering IDS occurrence. Spatial distributions of reserve lands and the maturity of highways network nearby may be more important than the length of railways when assessing the inhabitant vulnerability due to IDS. Highway length is the most decisive factor on IDS probability among all classes, with membership grades ranging from 0.99 to 0.55. Land surface temperature appears less effective for the identification of smaller scale IDS. NTL is more prominent on IDS probability in the "very high" class, with a membership grade of 0.80. The finding suggests that populated areas represented by NTL is a priori of IDS.

Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation.

The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the relationship of testing dataset variance on predictive neural network model performance. It is hypothesized that the dataset characteristics and variances may dictate the necessity of k-fold cross validation on neural network waste model construction. Seven RNN-LSTM predictive models were developed using historical landfill waste records and climatic and socio-economic data. The performance of all trials was acceptable in the training and validation stages, with MAPE all less than 10%. In this study, the 7-fold cross validation reduced the bias in selection of testing sets as it helps to reduce MAPE by up to 44.57%, MSE by up to 54.15%, and increased R value by up to 8.33%. Correlation analysis suggests that fewer outliers and less variance of the testing dataset correlated well with lower modeling error. The length of the continuous high waste season and length of total high waste period appear not important to the model performance. The result suggests that k-fold cross validation should be applied to testing datasets with higher variances. The use of MSE as an evaluation index is recommended.

The use of a recurrent neural network model with separated time-series and lagged daily inputs for waste disposal rates modeling during COVID-19.

A new modeling framework is proposed to estimate mixed waste disposal rates in a Canadian capital city during the pandemic. Different Recurrent Neural Network models were developed using climatic, socioeconomic, and COVID-19 related daily variables with different input lag times and study periods. It is hypothesized that the use of distinct time series and lagged inputs may improve modeling accuracy. Considering the entire 7.5-year period from Jan 2013 to Sept 2020, multi-variate weekday models were sensitive with lag times in the testing stage. It appears that the selection of input variables is more important than waste model complexity. Models applying COVID-19 related inputs generally had better performance, with average MAPE of 10.1%. The optimized lag times are however similar between the periods, with slightly longer average lag for the COVID-19 at 5.3 days. Simpler models with least input variables appear to better simulate waste disposal rates, and both 'Temp-Hum' (Temperature-Humidity) and 'Temp-New Test' (Temperature-COVID new test case) models capture the general disposal trend well, with MAPE of 10.3% and 9.4%, respectively. The benefits of the use of separated time series inputs are more apparent during the COVID-19 period, with noticeable decrease in modeling error.

Modeling of municipal waste disposal rates during COVID-19 using separated waste fraction models.

Municipal waste disposal behaviors in Regina, the capital city of Saskatchewan, Canada have significantly changed during the COVID-19 pandemic. About 7.5 year of waste disposal data at the Regina landfill was collected, verified, and consolidated. Four modeling approaches were examined to predict total waste disposal at the Regina landfill during the COVID-19 period, including (i) continuous total (Baseline), (ii) continuous fraction, (iii) truncated total, and (iv) truncated fraction. A single feature input recurrent neural network model was adopted for each approach. It is hypothesized that waste quantity modeling using different waste fractions and separate time series can better capture disposal behaviors of residents during the lockdown. Compared to the baseline approach, the use of waste fractions in modeling improves both result accuracy and precision. In general, the use of continuous time series over-predicted total waste disposal, especially when actual disposal rates were less than 50 t/day. Compared to the baseline approach, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were reduced. The R value increased from 0.63 to 0.79. Comparing to the baseline, the truncated total and the truncated fraction approaches better captured the total waste disposal behaviors during the COVID-19 period, probably due to the periodicity of the weeklong data set. For both approaches, MAE and MAPE were lower than 70 and 22%, respectively. The model performance of the truncated fraction appears the best, with an MAPE of 19.8% and R value of 0.92. Results suggest the uses of waste fractions and separated time series are beneficial, especially if the input set is heavily skewed.

Identification of behaviour patterns in waste collection and disposal during the first wave of COVID-19 in Regina, Saskatchewan, Canada.

The novel coronavirus (2019-nCov) has had significant impacts on almost every aspect of daily life. From 'stay-at-home' orders to the progressive lifting of restrictions, the COVID-19 pandemic has had unprecedented effects on consumer behaviours and waste disposal habits. The purpose of this short communication is to examine time series waste collection and disposal data in a mid-sized Canadian city to understand how behavioural changes have affected municipal waste management. The results suggest that private waste disposal increased during the pandemic. This may be due to people doing home renovations in order to accommodate working from home. Furthermore, it appears that changes in consumer habits destabilized the consistency of waste disposal tonnage when compared to the same time period in 2019. When considering curbside residential waste collection, there was also an increase in tonnage. This may be the result of more waste being generated at home due to changes in eating and cooking habits, and cleaning routine. Finally, the ratio of residential waste collection to total disposal is examined. More residential waste is being generated, which may have environmental and operational effects, especially related to collection and transportation. The results from this study are important from an operational perspective, and will help planners and policy makers to better prepare for changes in the waste stream due to pandemics or other emergencies.

Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands.

Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C-29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.

Waste disposal characteristics and data variability in a mid-sized Canadian city during COVID-19.

COVID-19, declared a global pandemic by the World Health Organization, has caused governments to react swiftly with a variety of measures to quell the spread of the virus. This study investigates changes in waste disposal characteristics and the relationship between the mass of biomedical waste disposed and new COVID-19 tests performed in Regina, Canada. Results suggest that between May and September 2020, significant differences in the median amount of waste disposed exist. The amount of monthly waste disposed was slightly lower to about 450-550 tonnes/month. Monthly waste data variability, however, was significantly lower. Seasonal effects on total waste disposal is observed, but is less obvious than pre-COVID time. Furthermore, the distribution of different waste fractions varies, probably due to operational and industrial characteristics. A non-linear relationship exists between the number of COVID-19 tests performed and the mass of biomedical waste disposed, perhaps due to a lagged relationship between biomedical waste generation and disposal.

Interactions of residential waste composition and collection truck compartment design on GIS route optimization.

Waste collection is an important functional element in a modern waste management system; and may account for up to half of the total expenditure on waste management in industrialized nations. Most optimization of waste collection studies include truck route distance and fuel consumption considerations without explicitly considering the inter-relationships of the model parameters. This study however delineates the complex inter-relationships of waste composition, collection frequency, collection type, and truck compartment configurations in a small waste collection zone in Austin, Texas. A total of 48 different scenarios are modelled and investigated. Truck travel distances are found sensitive to collection frequency, truck capacity, volume ratio of truck compartment, and waste density. The results showed that the increase in waste density and waste collection frequency helped to save up to 18.2% in travel distances and 41.9% in travel time. Waste composition is significant in travel distance, regardless of truck design. Increasing truck capacity by 25% helped to save 4.1-24.4% of truck travel distances. Optimal volume ratio of truck compartments was 50:50 (50% volume for garbage and 50% volume for recyclables); a finding that is different than what is currently reported in the literature; pointing to the site-specific nature of studies of this type. The use of dual compartment trucks helps to reduce travel distances by up to 23.0% and travel time by up to 14.3%. It appears that the minimization of operation time within the collection area is key to an efficient system.

Siting and ranking municipal landfill sites in regional scale using nighttime satellite imagery.

In 2016, about 24.9 million tonnes of solid waste were disposed of in Canadian landfills, where landfill technology is a common choice. This study aims to develop a data-driven GIS-based method that considers spatial, environmental, and economic constraints using study regions derived from night time light data for a 40 km buffer around Regina, Saskatchewan, Canada. Unlike other similar studies, this site suitability study assumes no political or administrative boundaries as inputs. Road network stands as the most decisive factor that accounts for 0.239 of entire weight, followed by protective areas with a total weight of 0.220. The regions that ranked the best for siting new landfills were generally located far from predominant water resources and protected areas, but are in the vicinity of major road networks, but are also far from urbanized regions. The sensitivity analysis showed that, overall, road network and protected areas are the most essential layers in this analysis. For the environmental group, protected areas and water resources are major layers. For the economic group, road network and surface temperature are the most important. The method presented in this study can easily accommodate other data sets based on importance in any given area.

Effects of groundwater metal contaminant spatial distribution on overlaying kriged maps.

Groundwater is a major source of drinking water for many Canadians, and contamination by heavy metals poses a significant risk to people and the environment. In this study, three water quality indices are studied in the vicinity of an unlined landfill in a semiarid climate. The study investigates indices using geostatistical analysis and ordinary kriging. This study employs a novel coupling technique in order to compare the index-based maps to a groundwater quality map from overlapping heavy metal kriged maps. A total of 11 heavy metals were evaluated in preliminary analysis, but only four (Mn, As, Fe, and U) had higher concentrations than allowable limits in some or all of the monitoring wells at the site. Results from mean-based classification of indices suggest the aquifer in proximity to the landfill has been impacted by metal contaminants. Kriged maps show that the spatial variations of Mn and U are similar, while results of Fe and As are also similar. However, the two sets of maps have distinctly different patterns. Maps for indices show an elevated plateau extending from the unlined landfill to the southeast corner, implying that the landfill may have negatively impacted groundwater quality. A groundwater quality map is developed by overlaying the heavy metal maps. The resulting map shows that the north and west parts of the study have lower groundwater pollution with respect to metal contaminants. The groundwater quality map may be more applicable for practitioners who need comprehensive water quality measurement.

An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill.

Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and surface water sources. The Regina landfill is built on top of the Condie aquifer, without an engineered liner. Missing data and inconsistent sampling make a traditional groundwater assessment difficult. An integrated statistical approach using principle component analysis, correlation analysis, ion plots, and multiple linear regression is used to study groundwater contamination at the Regina landfill. Geological locations of the water samples were explicitly considered. The abundance of cations in the groundwater was Ca2+ > Mg2+ > Na+ > K+ > Mn2+; and for anions SO42- > HCO3- > Cl-. Correlation analysis and ion plots pointed to gypsum and halite dissolution being the main factors affecting groundwater chemistry. Principal component analysis yielded three principal components, responsible for 80.7% of the total variance. For all monitoring well groups, the sodium absorption ratio was generally less than one. The variation in the ratio from monitoring well groups suggests possible groundwater contamination from landfill operation. Wilcox diagrams indicate groundwater near the landfill is unsuitable for irrigation. A two-step multiple linear regression was used to develop a model for total hardness prediction.

Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer.

TDS is modeled for an aquifer near an unlined landfill in Canada. Canadian Drinking Water Guidelines and other indices are used to evaluate TDS concentrations in 27 monitoring wells surrounding the landfill. This study aims to predict TDS concentrations using three different modeling approaches: dual-step multiple linear regression (MLR), hybrid principal component regression (PCR), and backpropagation neural networks (BPNN). An analysis of the bias and precision of each models follows, using performance evaluation metrics and statistical indices. TDS is one of the most important parameters in assessing suitability of water for irrigation, and for overall groundwater quality assessment. Good agreement was observed between the MLR1 model and field data, although multicollinearity issues exist. Percentage errors of hybrid PCR were comparable to the dual-step MLR method. Percentage error for hybrid PCR was found to be inversely proportional to TDS concentrations, which was not observed for dual-step MLR. Larger errors were obtained from the BPNN models, and higher percentage errors were observed in monitoring wells with lower TDS concentrations. All models in this study adequately describe the data in testing stage (R2 > 0.86). Generally, the dual-step MLR and hybrid PCR models fared better (R2avg = 0.981 and 0.974, respectively), while BPNN models performed worse (R2avg = 0.904). For this dataset, both regression and machine learning models are more suited to predict mid-range data compared to extreme values. Advanced regression methods (hybrid PCR and dual-step MLR) are more advantageous compared to BPNN.

Optimization of first order decay gas generation model parameters for landfills located in cold semi-arid climates.

Canada has one of the highest waste generation rates in the world. Because of high land availability, land disposal rates in the province of Saskatchewan are high compared to the rest of the country. In this study, landfill gas data was collected at semi-arid landfills in Regina and Saskatoon, Saskatchewan, and curve fitting was carried out to find optimal k and Lo or DOC values using LandGEM, Afvalzorg Simple, and IPCC first order decay models. Model parameters at each landfill were estimated and compared using default k and Lo or DOC values. Methane generation rates were substantially overestimated using default values (with percentage errors from 55 to 135%). The mean percentage errors for the optimized k and Lo or DOC values ranged from 11.60% to 19.93% at the Regina landfill, and 1.65% to 10.83% at the Saskatoon landfill. Finally, the effect of different iterative methods on the curve fitting process was examined. The residual sum of squares for each model and iterative approaches were similar, with the exception of iterative method 1 for the IPCC model. The default values in these models fail to represent landfills located in cold semi-arid climates. The use of site specific data, provided enough information is available regarding waste mass and composition, can greatly help to improve the accuracy of these first order decay models.

Alternative carbon dioxide modelling approaches accounting for high residual gases in LandGEM.

High Canadian waste disposal rates necessitate landfill gas monitoring and accurate forecasting. CO2 estimates in LandGEM version 3.02 currently rest on the assumptions that CO2 is a function of CH4, where the two gases make up nearly 100% of landfill gas content, leading to overestimated CO2 collection estimates. A total of 25 cases (five formulas, five approaches) compared annual CO2 collection at four western Canadian landfills. Despite common use in literature, the 1:1 ratio of CH4 to CO2 was not recommended to forecast landfill gas collection in cold climates. The existing modelling approach significantly overestimated CO2 production in three of four sites, resulting in the highest residual sum of squares. Optimization resulted in the most accurate results for all formulas and approaches, which had the greatest reduction in residual sums of squares (RSS) over the default approach (60.1 to 97.7%). The 1.4 Ratio approach for L o:L o-CO2 yielded the second most accurate results for CO2 flow (mean RSS reduction of 50.2% for all sites and subsection models). The annual k-modified LandGEM calculated k's via two empirical formulas (based on precipitation) and yielded the lowest accuracy in 12 of 20 approaches. Unlike other studies, strong relationships between optimized annual k's and precipitation were not observed.

Chronic invasive fungal sinusitis causing a pathologic Le Fort I fracture in an immunocompetent patient.

We describe the case of a 77-year-old immunocompetent woman with a history of chronic rhinosinusitis who presented with a pathologic Le Fort I fracture after a forceful sneeze. Imaging revealed diffuse sinus opacification and a Le Fort type I complex fracture involving the maxilla, pterygoid plates, clivus, and right nasal bridge. The patient underwent endoscopic debridement of her sinuses, which revealed mucosal dehiscence and otherwise normal healthy bleeding tissue. Anatomic pathology identified necrotic bone with invasive fungal hyphae. Cultures demonstrated Burkholderia cepacia, diphtheroid organisms, and Enterococcus and Serratia spp. The patient was administered an intravenous antibiotic and antifungal for several months, but interval imaging found no significant improvement in bone healing although the stability of her palate had improved on clinical examination. Chronic rhinosinusitis has been found to be a complication of soft-tissue, orbital, and intracranial infections but, to the best of our knowledge, a pathologic facial fracture secondary to chronic invasive fungal and bacterial rhinosinusitis has not been previously reported in the literature.

Long-term follow-up of vocal fold movement impairment and feeding after neonatal cardiac surgery.

OBJECTIVE: To determine the long-term prognosis of children with vocal fold mobility impairment (VFMI) after cardiac surgery, with respect to time to normal feeding and incidence of admissions for pneumonia and feeding difficulties. METHODS: A retrospective chart review was conducted of all neonates who had otolaryngology exam after cardiac surgery at a tertiary children's hospital from May 2007 to May 2008. Charts were reviewed for demographics, type of cardiac surgery, vocal fold mobility, diet at time of discharge and at last follow-up, time to full oral feeding, and hospital admissions. RESULTS: There were a total of 94 patients included in the study, 17 of whom had VFMI. While significantly more patients with VFMI required modified diet at discharge, 48% compared to 19% of patients with normal vocal fold mobility; there was no statistically significant difference in time to regular diet on long-term follow-up, 0.8 years (VFMI) compared to 0.4 years (normal vocal fold mobility). Of the 25 patients with modified diet or gastrostomy tube at discharge, 52% returned to full feeds within a year. There was no difference in hospitalizations for pneumonia in patients with or without VFMI. However in patients with VFMI, 35% required readmission for feeding difficulty or poor weight gain compared to only 5% in the infants with normal vocal fold mobility. CONCLUSION: After neonatal cardiac surgery, there do not appear to be long-term effects of VFMI with regards to readmission for pneumonia. However, there is an increased risk for hospitalization with respect to feeding difficulties in those neonates with VFMI. The overall prognosis for time to oral feeding is good.

Congenital hairy polyp of the oropharynx presenting as an esophageal mass in a neonate, a case report and literature review.

PURPOSE: To review the literature of congenital hairy polyps and describe the clinical presentation, operative management, and histologic findings of a congenital hairy polyp arising from the palatopharyngeus muscle in a neonate with recurrent choking episodes. METHODS: Chart review of a 2-month-old male referred to a tertiary care pediatric hospital. RESULTS: We present a case of a 2-month-old male who presented to the emergency room with recurrent episodes of choking and vomiting. The patient was previously healthy with no prior medical or neonatal history. The parents noted a small fleshy mass in the patient's oropharynx that he would chew on and swallow after several minutes. However, on physical exam, there was no evidence of oropharyngeal mass. The patient did not have respiratory distress. Imaging revealed a 22×7×11mm oblong, fatty mass in the lower cervical and upper thoracic esophagus with a thin stalk extending proximally to the upper collapsed esophagus. Intraoperative recorded laryngoscopy revealed a pedunculated soft palate mass attached to the right superior palatopharyngeus muscle. Histopathology revealed ectodermal and mesodermal elements in a polypoid structure lined by keratinizing squamous epithelium with adnexal structures and central mature adipose tissue, consistent with congenital hairy polyp resembling an accessory tragus of the ear and branchial anomaly. At 6-week follow up, the patient was doing well and gaining weight appropriately with no further choking episodes. There was no evidence of velopharyngeal dysfunction on follow up exam. The surgical site was completely healed and there was no evidence of recurrence. DISCUSSION: Congenital hairy polyps of the naso- and oropharynx are rare but may present as airway or esophageal masses, causing respiratory distress or choking episodes in a pediatric patient. The pathologic findings of keratinizing squamous epithelium, adnexal structures, adipose and cartilage tissues resemble congenital accessory tragus and may be considered a branchial arch anomaly.