Exposure assessment of four pharmaceutical powders based on dustiness and evaluation of damaged HEPA filters

Management of sugar dust in the sugar industry

Sugar dust poses significant risks in the sugar industry, threatening workers' safety and health as well as the potential for explosions and fires. The combustibility of sugar dust arises from its small, lightweight particles that disperse easily and ignite readily. Effective management strategies are essential to ensuring a safe work environment and preventing accidents. This perspective article provides an overview of sugar dust management in the global sugar industry. Various methods are employed to collect and manage sugar dust, including dust collectors, air handling systems, and proper housekeeping procedures. Advancements like electrostatic precipitators, high-efficiency particulate air filters, and self-cleaning dust collection systems show promise for future management. Utilizing both artificial intelligence and nanotechnology can also contribute to minimizing the concentrations of sugar dust in facilities. Stringent regulations and guidelines exist to control dust explosions in the industry. Implementation of robust safety measures and training programs significantly curbs the economic and environmental toll of sugar dust explosions. The paper concludes with recommendations to address sugar dust challenges, including enhanced regulation, investment in technology and research, and improved collaboration among industry stakeholders. These measures will mitigate hazards, ensure worker well-being, and safeguard the sugar industry's operations.

Use of the dustiness index in combination with the handling energy factor for exposure modelling of nanomaterials

The use of modelling tools in the occupational hygiene community has increased in the last years to comply with the different existing regulations. However, limitations still exist mainly due to the difficulty to obtain certain key parameters such as the emission rate, which in the case of powder handling can be estimated using the dustiness index (DI). The goal of this work is to explore the applicability and usability of the DI for emission source characterization and occupational exposure prediction to particles during nanomaterial powder handling. Modelling of occupational exposure concentrations of 13 case scenarios was performed using a two-box model as well as three nano-specific tools (Stoffenmanager nano, NanoSafer and GUIDEnano). The improvement of modelling performance by using a derived handling energy factor (H) was explored. Results show the usability of the DI for emission source characterization and respirable mass exposure modelling of powder handling scenarios of nanomaterials. A clear improvement in modelling outcome was obtained when using derived quartile-3 H factors with, 1) Pearson correlations of 0.88 vs. 0.52 (not using H), and 2) ratio of modelled/measured concentrations ranging from 0.9 to 10 in 75% cases vs. 16.7% of the cases when not using H. Particle number concentrations were generally underpredicted. Using the most conservative H values, predictions with ratios modelled/measured concentrations of 0.4-3.6 were obtained.

Assessment of exposure determinants and exposure levels by using stationary concentration measurements and a probabilistic near-field/far-field exposure model

Background: The Registration, Evaluation, Authorization and Restriction of Chemicals (REACH) regulation requires the establishment of Conditions of Use (CoU) for all exposure scenarios to ensure good communication of safe working practices. Setting CoU requires the risk assessment of all relevant Contributing Scenarios (CSs) in the exposure scenario. A new CS has to be created whenever an Operational Condition (OC) is changed, resulting in an excessive number of exposure assessments. An efficient solution is to quantify OC concentrations and to identify reasonable worst-case scenarios with probabilistic exposure modeling. Methods: Here, we appoint CoU for powder pouring during the industrial manufacturing of a paint batch by quantifying OC exposure levels and exposure determinants. The quantification was performed by using stationary measurements and a probabilistic Near-Field/Far-Field (NF/FF) exposure model. Work shift and OC concentration levels were quantified for pouring TiO 2 from big bags and small bags, pouring Micro Mica from small bags, and cleaning. The impact of exposure determinants on NF concentration level was quantified by (1) assessing exposure determinants correlation with the NF exposure level and (2) by performing simulations with different OCs. Results: Emission rate, air mixing between NF and FF and local ventilation were the most relevant exposure determinants affecting NF concentrations. Potentially risky OCs were identified by performing Reasonable Worst Case (RWC) simulations and by comparing the exposure 95 th percentile distribution with 10% of the occupational exposure limit value (OELV). The CS was shown safe except in RWC scenario (ventilation rate from 0.4 to 1.6 1/h, 100 m 3 room, no local ventilation, and NF ventilation of 1.6 m 3/min). Conclusions: The CoU assessment was considered to comply with European Chemicals Agency (ECHA) legislation and EN 689 exposure assessment strategy for testing compliance with OEL values. One RWC scenario would require measurements since the exposure level was 12.5% of the OELV.

On the Relationship between Exposure to Particles and Dustiness during Handling of Powders in Industrial Settings

Exposure to ceramic powders, which is frequent during handling operations, is known to cause adverse health effects. Finding proxy parameters to quantify exposure is useful for efficient and timely exposure assessments. Worker exposure during handling of five materials [a silica sand (SI1), three quartzes (Q1, Q2, and Q3), and a kaolin (K1)] with different particle shape (prismatic and platy) and sizes (3.4-120 µm) was assessed. Materials handling was simulated using a dry pendular mill under two different energy settings (low and high). Three repetitions of two kilos of material were carried out per material and energy conditions with a flow rate of 8-11 kg h-1. The performance of the dustiness index as a predictor of worker exposure was evaluated correlating material's dustiness indexes (with rotating drum and continuous drop) with exposure concentrations. Significant impacts on worker exposure in terms of inhalable and respirable mass fractions were detected for all materials. Mean inhalable mass concentrations during background were always lower than 40 µg m-3 whereas during material handling under high energy settings mean concentrations were 187, 373, 243, 156, and 430 µg m-3 for SI1, Q1, Q2, Q3, and K1, respectively. Impacts were not significant with regard to particle number concentration: background particle number concentrations ranged between 10 620 and 46 421 cm-3 while during handling under high energy settings they were 20 880 - 40 498 cm-3. Mean lung deposited surface area during background ranged between 27 and 101 μm2 cm-3 whereas it ranged between 22 and 42 μm2 cm-3 during materials handling. TEM images evidenced the presence of nanoparticles (≤100 nm) in the form of aggregates (300 nm-1 µm) in the worker area, and a slight reduction on mean particle size during handling was detected. Dustiness and exposure concentrations showed a high degree of correlation (R2 = 0.77-0.97) for the materials and operating conditions assessed, suggesting that dustiness could be considered a relevant predictor for workplace exposure. Nevertheless, the relationship between dustiness and exposure is complex and should be assessed for each process, taking into account not only material behaviour but also energy settings and workplace characteristics.

Health risk assessment from exposure to particles during packing in working environments

Packing of raw materials in work environments is a known source of potential health impacts (respiratory, cardiovascular) due to exposure to airborne particles. This activity was selected to test different exposure and risk assessment tools, aiming to understand the effectiveness of source enclosure as a strategy to mitigate particle release. Worker exposure to particle mass and number concentrations was monitored during packing of 7 ceramic materials in 3 packing lines in different settings, with low (L), medium (M) and high (H) degrees of source enclosure. Results showed that packing lines L and M significantly increased exposure concentrations (119-609 μg m^-3 respirable, 1150-4705 μg m^-3 inhalable, 24,755-51,645 cm^-3 particle number), while non-significant increases were detected in line H. These results evidence the effectiveness of source enclosure as a mitigation strategy, in the case of packing of ceramic materials. Total deposited particle surface area during packing ranged between 5.4 and 11.8 × 10⁵ μm² min^-1, with particles depositing mainly in the alveoli (51-64%) followed by head airways (27-41%) and trachea bronchi (7-10%). The comparison between the results from different risk assessment tools (Stoffenmanager, ART, NanoSafer) and the actual measured exposure concentrations evidenced that all of the tools overestimated exposure concentrations, by factors of 1.5-8. Further research is necessary to bridge the current gap between measured and modelled health risk assessments.

Source specific exposure and risk assessment for indoor aerosols

Poor air quality is a leading contributor to the global disease burden and total number of deaths worldwide. Humans spend most of their time in built environments where the majority of the inhalation exposure occurs. Indoor Air Quality (IAQ) is challenged by outdoor air pollution entering indoors through ventilation and infiltration and by indoor emission sources. The aim of this study was to understand the current knowledge level and gaps regarding effective approaches to improve IAQ. Emission regulations currently focus on outdoor emissions, whereas quantitative understanding of emissions from indoor sources is generally lacking. Therefore, specific indoor sources need to be identified, characterized, and quantified according to their environmental and human health impact. The emission sources should be stored in terms of relevant metrics and statistics in an easily accessible format that is applicable for source specific exposure assessment by using mathematical mass balance modelings. This forms a foundation for comprehensive risk assessment and efficient interventions. For such a general exposure assessment model we need 1) systematic methods for indoor aerosol emission source assessment, 2) source emission documentation in terms of relevant a) aerosol metrics and b) biological metrics, 3) default model parameterization for predictive exposure modeling, 4) other needs related to aerosol characterization techniques and modeling methods. Such a general exposure assessment model can be applicable for private, public, and occupational indoor exposure assessment, making it a valuable tool for public health professionals, product safety designers, industrial hygienists, building scientists, and environmental consultants working in the field of IAQ and health.

Pharmaceutical dust exposure at pharmacies using automatic dispensing machines: a preliminary study

Automatic dispensing machines (ADMs) used in pharmacies concentrate and dispense large volumes of pharmaceuticals, including uncoated tablets that can shed dust. We evaluated 43 employees' exposures to pharmaceutical dust at three pharmacies where ADMs were used. We used an optical particle counter to identify tasks that generated pharmaceutical dust. We collected 72 inhalable dust air samples in or near the employees' breathing zones. In addition to gravimetric analysis, our contract laboratory used internal methods involving liquid chromatography to analyze these samples for active pharmaceutical ingredients (APIs) and/or lactose, an inactive filler in tablets. We had to choose samples for these additional analyses because many methods used different extraction solvents. We selected 57 samples for analysis of lactose. We used real-time particle monitoring results, observations, and information from employees on the dustiness of pharmaceuticals to select 28 samples (including 13 samples that were analyzed for lactose) for analysis of specific APIs. Pharmaceutical dust was generated during a variety of tasks like emptying and refilling of ADM canisters. Using compressed air to clean canisters and manual count machines produced the overall highest peak number concentrations (19,000-580,000 particles/L) of smallest particles (count median aerodynamic diameter ≤ 2 μm). Employees who refilled, cleaned, or repaired ADM canisters, or hand filled prescriptions were exposed to higher median air concentrations of lactose (5.0-12 μg/m(3)) than employees who did other jobs (0.04-1.3 μg/m(3)), such as administrative/office work, labeling/packaging, and verifying prescriptions. We detected 10 APIs in air, including lisinopril, a drug prescribed for high blood pressure, levothyroxine, a drug prescribed for hypothyroidism, and methotrexate, a hazardous drug prescribed for cancer and other disorders. Three air concentrations of lisinopril (1.8-2.7 μg/m(3)) exceeded the lower bound of the manufacturer's hazard control band (1-10 μg/m(3)). All other API air concentrations were below applicable occupational exposure limits. Our findings indicate that some pharmacy employees are exposed to multiple APIs and that measures are needed to control those exposures.