Aerosol Control

Abstract

An air quality monitoring system comprising: a plurality of particulate matter (PM) sensors disposed at corresponding locations in a monitored area; and an air quality processing device coupled to each of the plurality of PM sensors via a communications network, the air quality processing device configured to receive particle level signals from at least two of the plurality of PM sensors and determine a particulate matter flow between the at least two PM sensors based on the corresponding particle level signals.

Description

[Technical field]

[0001] The present disclosure relates to an air quality system, an air quality monitoring system, and a method for controlling and monitoring air quality. [Background technology]

[0002] Aerosols are known vectors for many diseases. Infectious pathogens can include viruses, bacteria, or fungi, which can be spread through breathing, talking, coughing, sneezing, dust generation, flushing the toilet, or any activity that generates aerosol particles or droplets.

[0003] Aerosols can pose an infection risk in many indoor environments, such as commercial, educational, and healthcare settings. Aerosols can pose a particular infection risk in healthcare settings, such as hospitals, where infected patients can excrete pathogens that can be transmitted by aerosols and infect hospital staff, visitors, and other patients. Healthcare settings typically have high traffic and provide large populations for disease transmission. Healthcare settings also typically use industrial air conditioning and heating systems that can carry aerosols over long distances, widening the area of ​​potential transmission. Methicillin-resistant Staphylococcus aureus (MRSA) and COVID-19 are well-known examples of pathogens that can spread rapidly within healthcare settings via airborne transmission.

[0004] It is desirable to monitor aerosol flow in indoor environments. It is also desirable to understand high risk events associated with high levels of aerosol generation. Furthermore, it is desirable to control aerosol flow in indoor environments. The disclosed systems and methods can provide one or more of these desirable effects. Summary of the Invention

[0005] According to a first aspect of the present disclosure, there is provided an air quality monitoring system, the air quality monitoring system comprising: a plurality of particulate matter (PM) sensors disposed at corresponding locations within the monitored area; and an air quality processing device coupled to each of a plurality of PM sensors via a communications network, receiving particulate level signals from at least two of the plurality of PM sensors; determining a flow of particulate matter between the at least two PM sensors based on the corresponding particulate level signals; Equipped with an air quality treatment device.

[0006] Air quality monitoring systems can advantageously track aerosol flow within a monitored area. Determining and understanding such aerosol flow in a healthcare environment can provide several benefits, such as understanding risk factors associated with aerosol generation, enabling design mitigation measures to reduce aerosol generation and aerosol flow, and enabling intervention measures to be initiated to reduce particulate matter and aerosol flow.

[0007] The air quality processing device can be configured to determine a flow of particulate matter between the at least two PM sensors by detecting an aerosol event at a first of the at least two PM sensors based on a particle level signal exceeding a first event threshold, and detecting the aerosol event at a second of the at least two PM sensors based on a particle level signal exceeding a second event threshold.

[0008] The first event threshold may include an adaptive event threshold.

[0009] The second event threshold may comprise a scaled value of the first event threshold.

[0010] The air quality processing device may be configured to determine the flow of particulate matter based on the delay and / or amplitude difference between corresponding peaks of the particulate level signal.

[0011] The air quality processing device may be configured to determine particulate matter flow by applying cross-correlation to particle level signals associated with at least two PM sensors.

[0012] The air quality processing device may be configured to identify one or more of a source of the particulate matter flow, a path of the particulate matter flow, a velocity of the particulate matter flow, a attenuation of the particulate matter flow, and / or one or more predicted destinations of the particulate matter flow.

[0013] The air quality processing device may be further configured to output an intervention signal configured to operate one or more air quality intervention mechanisms.

[0014] The air quality intervention mechanism may comprise one or more of an automatic door or its actuator, an operating parameter of an air filtration device, an operating parameter of a heating, ventilation and air conditioning (HVAC) system, and a warning signal.

[0015] The warning signal may include an audible and / or visual alarm signal.

[0016] The warning signal may include an information signal.

[0017] The air quality processing device may be configured to output an intervention signal to operate one or more air quality intervention mechanisms at a location associated with a source of the particulate matter, a location associated with a path of the particulate matter flow, and / or a location associated with one or more potential destinations of the particulate matter flow.

[0018] The air treatment device may be further configured to analyze the particulate level signals of the one or more PM sensors over a period of time to determine an abundance of particulate matter associated with the one or more PM sensors, and output abundance data indicative of the abundance of particulate matter.

[0019] The abundance data may indicate high risk areas of the monitored area corresponding to one or more PM sensors having an abundance of particulate matter above a first abundance threshold, and / or low risk areas of the monitored area corresponding to one or more PM sensors having an abundance of particulate matter below a second abundance threshold.

[0020] The abundance of particulate matter can include periodic aerosol events associated with one or more PM sensors. The abundance data can indicate the periodic aerosol events, times of occurrence of the periodic aerosol events, and / or locations of the one or more PM sensors associated with the periodic aerosol events.

[0021] The air treatment device can be configured to output an intervention signal for operating one or more intervention mechanisms at times corresponding to periodic aerosol events.

[0022] The air treatment device receives operational data for the monitored area, correlates one or more aerosol events with the operational data, and identifies an aerosol event trigger based on the correlation.

[0023] Each of the PM sensors can be configured to measure the concentration of particulate matter in the air having particle sizes ranging from a lower detection limit of the PM sensor to a particulate matter rating.

[0024] Each PM sensor may be provided with multiple particulate matter ratings and may be configured to measure multiple concentrations of particulate matter in the air within multiple corresponding particle size ranges.

[0025] Particulate matter ratings may include one or more of 0.5 μm, 1.0 μm, 2.5 μm, 4.0 μm, 10.0 μm, 25.0 μm, and 50 μm.

[0026] The lower detection limit may include any of 0.05 μm, 0.1 μm, 0.3 μm, and 0.5 μm.

[0027] Two or more of the PM sensors may be located at different heights.

[0028] The air quality monitoring system may further comprise a communications network.

[0029] The air quality monitoring system may further include a number of additional sensors, which may include one or more of a carbon dioxide (CO2) sensor, a humidity sensor, a temperature sensor, and an air pressure sensor.

[0030] The air quality monitoring system may comprise a number of sensor units, each of which may comprise one of a number of PM sensors and one or more further sensors.

[0031] According to a second aspect of the present disclosure, there is provided a method of monitoring a flow of particulate matter in a monitoring area, the method comprising: receiving a plurality of particulate level signals from at least two particulate matter (PM) sensors disposed within a monitored area; and determining a flow of particulate matter between the at least two PM sensors based on the corresponding particulate level signals.

[0032] The method may be computer implemented.

[0033] According to a third aspect of the present disclosure, there is provided an air quality monitoring system, the air quality monitoring system comprising: a plurality of particulate matter (PM) sensors disposed at corresponding locations within the monitored area; and an air quality processing device coupled to each of a plurality of PM sensors via a communications network, receiving a particulate level signal from each of a plurality of PM sensors; determining an aerosol event based on the at least one particle level signal; configured to output an intervention signal configured to operate one or more air quality intervention mechanisms; Equipped with an air quality treatment device.

[0034] According to a fourth aspect of the present disclosure, there is provided an air quality monitoring system, the air quality monitoring system comprising: a plurality of particulate matter (PM) sensors disposed at corresponding locations within the monitored area; and an air quality processing device coupled to each of a plurality of PM sensors via a communications network, receiving a particulate level signal from each of the plurality of PM sensors; analyzing the particulate level signals for the one or more PM sensors over a period of time to determine an abundance of particulate matter associated with the one or more PM sensors; configured to output abundance data indicative of an abundance of particulate matter; Equipped with an air quality treatment device.

[0035] According to a fifth aspect of the present disclosure, there is provided an air quality system, the air quality system comprising: a plurality of particulate matter (PM) sensors disposed at corresponding locations within the monitored area; and an air filtration device wirelessly coupled to at least one PM sensor, receiving a particulate level signal from one or more of the plurality of PM sensors; configured to adjust a fan speed of the air filtration device in response to the particulate level signal; Equipped with an air filtration device.

[0036] Adjusting the fan speed in response to the particulate level signal from the PM sensor can advantageously provide on-demand selective control of the air filtration device.

[0037] The air filtering device may be further configured to adjust a fan speed based on a distance between each of one or more of the plurality of PM sensors and the air filtering device.

[0038] The air filtering device may be configured to determine a distance between each of one or more of the plurality of PM sensors and the air filtering device based on the received signal strength indication of the particulate level signal.

[0039] Each of the PM sensors can be configured to measure the concentration of particulate matter in the air having particle sizes ranging from a lower detection limit of the PM sensor to a particulate matter rating.

[0040] Each PM sensor may be provided with multiple particulate matter ratings and may be configured to measure multiple concentrations of particulate matter in the air within multiple corresponding particle size ranges.

[0041] Particulate matter ratings may include one or more of 0.5 μm, 1.0 μm, 2.5 μm, 4.0 μm, 10.0 μm, 25.0 μm, and 50 μm.

[0042] The lower detection limit may include any of 0.05 μm, 0.1 μm, 0.3 μm, and 0.5 μm.

[0043] Each of the plurality of PM sensors can be configured to measure a concentration of particulate matter in the air for a plurality of particle sizes. The air filtering device can be configured to adjust a fan speed based on a distance between each of the one or more PM sensors of the plurality of PM sensors and the air filtering device and a concentration of particulate matter for each of a plurality of particle size ranges for each of the one or more PM sensors of the plurality of PM sensors.

[0044] The air quality system may further comprise a server, and the plurality of PM sensors and / or air filtration devices are communicatively coupled to the server via a communication network.

[0045] The air quality system may further comprise a number of additional sensors, which may comprise one or more of a carbon dioxide (CO2) sensor, a humidity sensor, a temperature sensor, and an air pressure sensor.

[0046] The air quality system may comprise a number of sensor units, each sensor unit comprising one of a number of PM sensors and one or more further sensors.

[0047] The air filtering device may comprise any of a ventilation system, a heating, ventilation and air conditioning (HVAC) system, and an air cleaner.

[0048] The air filtration device may comprise an air cleaner including one or more of a high efficiency particulate air (HEPA) filter, a carbon filter, and a UVC lamp.

[0049] Two or more of the PM sensors may be located at different heights.

[0050] A computer program can be provided that, when executed on a computer, causes the computer to configure any apparatus, including any circuit, controller, converter, or device disclosed herein, or to perform any method disclosed herein. The computer program can be implemented in software, and the computer can be considered as any suitable hardware, such as, by way of non-limiting examples, a digital signal processor, a microcontroller, and implementation in a read-only memory (ROM), an erasable programmable ROM (EPROM), or an electronically erasable programmable ROM (EEPROM). The software can be an assembly program.

[0051] The computer program may be provided on a computer readable medium, which may be a physical computer readable medium, such as a disk or memory device, or may be embodied as a transitory signal. Such a transitory signal may be a network download, such as an Internet download. One or more non-transitory computer readable storage media may be provided that store computer executable instructions that, when executed, cause a computing system to perform any of the methods disclosed herein.

[0052] One or more embodiments will now be described, by way of example only, with reference to the accompanying drawings. [Brief description of the drawings]

[0053] [Figure 1] 1 illustrates an air quality system for controlling aerosol flow in a healthcare setting, according to an embodiment of the present disclosure. [Diagram 2] 1 illustrates an air quality monitoring system according to an embodiment of the present disclosure. [Diagram 3] 1 illustrates another air quality monitoring system according to an embodiment of the present disclosure. [Figure 4] 4 illustrates particulate matter sensor data of an aerosol event captured by the air quality monitoring system of FIG. 3. [Diagram 5] 4 illustrates carbon dioxide sensor data of an aerosol event captured by the air quality monitoring system of FIG. 3. [Figure 6] 4A and 4B show cross-correlation between particulate sensor data for aerosol events captured for the pair of sensors in FIG. 3. [Figure 7] 4A and B show cross-correlation between particulate matter sensor data for captured aerosol events for another pair of sensors in FIG. 3. [Figure 8] 4A and 4B show cross-correlation between particulate matter sensor data for captured aerosol events for further pairs of sensors in FIG. [Figure 9] 4A and 4B show cross-correlation between particulate matter sensor data for captured aerosol events for yet another pair of sensors from FIG. 3 . [Figure 10] 5A-D show cross-correlation between particulate matter sensor data and CO2 sensor data for various pairs of sensors in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0054] FIG. 1 illustrates an air quality system 100 for controlling aerosol flow in an indoor environment, according to an embodiment of the present disclosure. In this example, the indoor environment is a healthcare environment 102 that includes a portion of a hospital ward, including a hallway 104 and a ward section 106. The air quality system 100 comprises a plurality of particulate matter (PM) sensors 108a-108n. In this example, the air quality system comprises fourteen PM sensors 108a-108n disposed at a corresponding plurality of locations in the healthcare environment 102. The fourteen PM sensors include twelve PM sensors 108a-108l in the ward section 106, two PM sensors adjacent each bed 110, and two PM sensors in the hallway. The air quality system 100 further comprises an air filtration device 112 wirelessly coupled to the PM sensors 108a-n (collectively PM sensors 108). The air filtering device 112 receives the particulate level signals from each of the sensors 108a-108n and adjusts a fan speed of the air filtering device 112 in response to the particulate level signals. The air filtering device 112 is disposed at a location away from the multiple locations of the PM sensors 108. In other words, the PM sensors 108 are remote from the air filtering device 112.

[0055] As disclosed herein, the air filtering device 112 can comprise any device capable of reducing PM content in the air. For example, the air filtering device 112 can comprise a ventilation system, a heating, ventilation and air conditioning (HVAC) system (such as those installed in commercial environments and hospitals), or a stand-alone air cleaner. The air filtering device 112 comprises a fan to draw air through the device. Increasing the speed of the fan provides an increased rate of air purification / PM reduction.

[0056] Adjusting the fan speed in response to the particle level signal from the PM sensor 108 advantageously provides on-demand selective control of the air filtering device 112. For example, the air filtering device 112 may increase the fan speed to provide a high PM reduction rate when the PM sensor 108 indicates a relatively high PM content, and decrease the fan speed to provide a reduced PM reduction rate when the PM sensor indicates a relatively low PM content. As a result, the air filtering device 112 may operate with reduced energy consumption and noise pollution while maintaining sufficient PM reduction during periods of relatively high PM content in the air.

[0057] Additionally, by monitoring the healthcare environment 102 with the PM sensor 108, the system 100 can advantageously monitor particles directly associated with infectious agents. For example, MRSA is known to be transmitted through dead skin, which may constitute particulate matter on the order of 25-50 μm in size. Airborne viruses such as COVID-19 have been shown to be associated with (inhalable) particulate matter sizes below 2.5 μm. The flow of infectious aerosols and particulate matter may be generated directly by a patient breathing, sneezing, coughing, etc., or indirectly by the agitation of items containing infectious particles, such as flapping bedsheets, drawing curtains, etc. The PM sensor 108 can advantageously detect all sources of particulate matter flow, in contrast to CO2 sensors, which may only detect areas of stagnant air / poor ventilation or the presence of large numbers of people.

[0058] As disclosed herein, particulate matter, particulate matter sensors, and specific material sensor ratings are referred to as understood in the art. Particulate matter may refer to a mixture of solid particles and liquid droplets found in the air. Particulate matter may refer to particle sizes greater than 0.1 μm, e.g., 0.1 μm to 50 μm. Particulate matter does not refer to individual molecules, such as molecules of CO2.

[0059] The PM sensor 108 can measure the concentration of particulate matter in the air (in micrograms per cubic meter) for a range of particle sizes. The PM sensor can measure the concentration of particulate matter for particle sizes between a lower detection limit and a particulate matter rating. The lower detection limit may be on the order of 0.1 μm, for example 0.05 μm, 0.1 μm, 0.3 μm, or 0.5 μm.

[0060] A particulate matter rating may indicate the upper particle size limit of a measurement range. As an example, a PM sensor 108 with a particulate matter rating of 2.5 μm (PM2.5) may measure the concentration of particulate matter having particle sizes ranging from the lower detection limit to 2.5 μm. The PM sensor 108 may have a particulate matter rating of any of 0.5 μm (PM0.5), 1.0 μm (PM1), 2.5 μm (PM2.5), 4.0 μm (PM4), 10.0 μm (PM10), 25.0 μm (PM25), and 50.0 μm (PM50). A PM sensor may have multiple particulate matter ratings corresponding to multiple particle size ranges. The particle level signal of each sensor may include a concentration value for each of one or more particle size ranges. Particulate matter having a particle size less than 10 μm may be referred to as inhalable, and particles less than 2.5 μm may be referred to as fine inhalable. Monitoring these particle sizes advantageously directly monitors particulate matter associated with airborne disease transmission.

[0061] The PM sensors 108 may be coupled to the air filtering devices 112 via a local wireless connection, such as a WiFi network, a Bluetooth® (classic or Bluetooth Low Energy) connection, or other known local wireless connection. The PM sensors 108 may each communicate directly with the air filtering devices 112 (rather than through a communications network).

[0062] In this example, the air filtering device 112 comprises an air cleaner located in the ward bay 106. The air cleaner may comprise one or more filtering mechanisms. The one or more filtering mechanisms may comprise any of a high efficiency particulate air (HEPA) filter, a carbon filter, and a UVC lamp.

[0063] The air filtering device 112 may adjust the fan speed based on the particle level signal from each PM sensor 108 and the distance between the PM sensor 108 and the air filtering device. In this manner, the system 100 may take into account the effectiveness of the PM reduction of the air filtering device 112 at the location of the associated PM sensor 108. In some examples, the fan speed may be based on the square of the distance to each PM sensor 108. For example, a function determining the fan speed may take the form:

number

[0064] In some examples, the air filtering device 112 may adjust the fan speed based on the distance to each of the PM sensors 108 and the corresponding concentration values ​​for each of a number of particle sizes. In one example, the function that determines the fan speed may take the following form:

number

[0065] In some examples, the functions f1 through f4 may take the following form:

number

[0066] In some examples, the PM sensors 108 can be fixedly positioned in predetermined locations such that the air filtering device 112 can store the distance to each sensor 108 in memory. In other examples, the PM sensors 108 can be positioned by a user. As a result, the air filtering device 112 can determine the distance to the PM sensors 108 based on a received signal strength indicator (RSSI) of the particle level signal. In this manner, a user can advantageously reposition the PM sensors 108 as desired. For example, in a hospital environment, the sensors can be repositioned to protect vulnerable patients.

[0067] In some examples, two or more PM sensors 108 can be placed at different heights. Two or more sensors can be placed on either side of the object or area being monitored, such as on either side of the bed 110 or on either side of the ward section 106. Placing two sensors at different heights can indicate the decay or fall rate of the aerosol. Fall rate may be related to (higher concentration of) larger particle size. Understanding the suspended levels of aerosols at different heights can indicate possible particulate matter movement or flow and associated risks. Different height sensors can also indicate an aerosol event from one side of the room and / or how aerosols can move from the foot of the bed to above the patient's head. Different height sensors can use a time course to track the aerosol flow and indicate the rate of potentially infectious aerosols. The time course can indicate the ventilation rate deliverable requirements of the air filtration unit 112 and / or the optimal location of the inlet of the air filtration unit to reduce or prevent aerosol movement across the monitored area 102.

[0068] In some examples, the system 100 can include one or more additional sensors. The one or more additional sensors can include multiple additional sensors located at multiple locations within the monitored area 102. In some examples, the one or more additional sensors can be co-located with each of the PM sensors 108. For example, each of the PM sensors 108 can form part of a sensor unit that includes one or more additional sensors. The one or more additional sensors can include one or more of a carbon dioxide (CO2) sensor, a humidity sensor, a temperature sensor, and an air pressure sensor. The air filtration device 112 can adjust a fan speed based on one or more additional signals corresponding to the one or more additional sensors. The one or more additional sensors can provide additional data to support the system 100. The temperature and humidity sensors can represent a controlled environment provided by the HVAC system and need to be stable throughout the ward section 106 to minimize possible convective flows and the resulting faster possible spread of infectious matter. The pressure sensor can be useful to monitor air dynamics, which can affect air flow and can change in response to the operation of the air filtration unit 112. The CO2 sensor can monitor CO2 concentrations that may be correlated with particulate matter PM1 or less. Thus, the CO2 sensor can provide an additional indication of increased risk and people's activity. For example, during peak times of the day, particulate levels should remain low due to the performance of the air filtration device 112, but CO2 levels may rise, which may be associated with any increase in particulate levels. The system 100 can output a signal indicative of the measurement of one or more additional sensors. The output signal can include a warning that a temperature, humidity, or pressure gradient has been detected. In some examples, the output signal can be provided to an HVAC system to correct the temperature, humidity, or pressure gradient.

[0069] In some examples, the air filtering device may include a PM sensor and / or one or more additional sensors.

[0070] In some examples, the system 100 may include one or more outdoor sensors, e.g., sensors located outside a hospital building. The one or more outdoor sensors may include one or more PM sensors and / or one or more additional sensors. The outdoor sensors may monitor humidity, wind currents, temperature, PM counts, pressure, etc. The outdoor sensors allow the system 100 to take seasonal changes in particle levels into account. For example, a temperature gradient between the outside and inside of a building may cause air currents that carry particulate matter. As a further example, background particle levels may change seasonally due to variations in pollen in the air.

[0071] In some examples, the system 100 may optionally include a server 114. The server 114 may be located in the medical environment 102 or may be located elsewhere (in the cloud). The sensor 108 and / or the air filtering device 112 may include a transceiver that allows communication with the server 114 via a communication network, such as a local area network or the Internet. The sensor 108 and / or the air filtering device 112 may output a particle level signal to the server 114 for storage, monitoring, analysis, and / or intervention. A second aspect of the present disclosure described below relates to a monitoring system including multiple PM sensors connected to an external air quality processing device via a communication network. It will be understood that the functions described in relation to the second aspect apply equally to the first aspect described in relation to FIG. 1.

[0072] Figure 2 illustrates an air quality monitoring system 200 according to an embodiment of the present disclosure. Features in Figure 2 that are present in Figure 1 have been given corresponding reference numbers in the 200 series and will not necessarily be described again here.

[0073] The air quality monitoring system 200 is configured to monitor aerosol flow in an indoor environment 202. In this example, the indoor environment comprises a healthcare environment 202 comprising a portion of a hospital ward comprising a hallway 204 and a ward section 206. The air quality monitoring system 200 comprises a plurality of PM sensors 208a-208n disposed at a corresponding plurality of locations within the healthcare environment 202. Each of the plurality of sensors 208 is coupled to a communication network 218. The air quality monitoring system 200 further comprises an air quality processor 216 coupled to each of the plurality of PM sensors by the communication network 218. The air quality processor 218 may comprise one or more processors located on a back-end server. The back-end server may be located in another portion of the healthcare environment or remotely relative to the healthcare environment 102, such as in the cloud. The air quality processor 218 may be configured to receive particle level signals from at least two of the plurality of PM sensors 208. The air quality processing unit 218 may process the particulate level signals and determine a flow of particulate matter between at least two of the PM sensors 208 based on the corresponding particulate level signals.

[0074] The air quality monitoring system 200 can advantageously track aerosol flow within a monitored area. Determining and understanding such aerosol flow in a healthcare environment can provide many benefits, such as understanding risk factors associated with aerosol generation, enabling design mitigation measures to reduce aerosol generation and aerosol flow, and enabling activation of intervention measures to reduce particulate matter and aerosol flow.

[0075] It will be understood that any of the features of system 100 (such as additional sensors and fan speed adjustments) and any of the features of PM sensor 108 (such as particle size ranges, advantages of monitoring specific particle size ranges, and positioning of the PM sensor) described above in connection with FIG. 1 are equally applicable to system 200 and PM sensor 208 of FIG. 2, and vice versa.

[0076] The communication network 218 may comprise a local area network and / or a wide area network such as the Internet. The communication network 218 may include wired and / or wireless communication paths. The communication network 218 may include a local gateway 220 (or hub) for (i) communicating locally with the multiple PM sensors 208, optionally via a wireless network such as WiFi, and (ii) communicating with the air quality treatment device 218 via a wide area network connection. In other examples, the PM sensors 208 may communicate directly with the air treatment device 216 via a wired network or through a wireless network such as a mobile communication network or a WiFi network.

[0077] The air quality monitoring system 200 can determine the flow of particulate matter between two or more PM sensors 208 based on the delay and / or amplitude difference between corresponding peaks in the particulate level signals. In some examples, the air quality processing device 216 can determine the flow of particulate matter between two PM sensors by: (i) detecting an aerosol event at a first PM sensor based on the particulate level signal of the first PM sensor exceeding a first event threshold; and (ii) detecting the same aerosol event at a second PM sensor based on the particulate level signal of the second PM sensor exceeding a second event threshold. The second event threshold may be less than or equal to the first event threshold.

[0078] As an example, if an aerosol (particulate matter) generating event (referred to as an aerosol event), such as a patient sneezing or shaking the bedding, occurs at a bed 210 adjacent to PM sensors 208h, 208k, the air quality processor 216 may detect a peak on the first PM sensor 208h adjacent to the bed 210 based on a particle level signal that exceeds a first event threshold. At a later point in time, the air quality processor 216 may detect a peak corresponding to the same aerosol event at one or more of the remaining sensors 208a-208g, 208i-208l in the ward section 206 based on a corresponding particle level signal that exceeds a second event threshold. At an even later point in time, the air quality processor 216 may detect a peak corresponding to the same aerosol event on a further PM sensor 208m located in the hallway 204 based on a corresponding particle level signal that exceeds a second event threshold. In this manner, the air quality monitoring system 200 may track aerosol flow from an aerosol event in the healthcare environment 202. Further discussion of exemplary data showing tracking of aerosol generating events is provided below in connection with Figures 3-10.

[0079] The first and second event thresholds enable the monitoring system 200 to detect an aerosol event as a peak in the particle level signal above an expected background level. The first and / or second event thresholds may include adaptive event level thresholds having values ​​that adapt according to changing background levels of particulate matter. For example, in a hospital setting, higher background levels may be expected during the day compared to night, and even higher background levels may be expected during visiting hours or ward rounds, etc. The first and / or second event thresholds may include time-dependent adaptive thresholds that vary according to the time of day and / or the particular day (weekend vs. weekday). The threshold levels may be determined following an initial calibration period after installation of the system 200.

[0080] The air handling device 216 can identify one or more parameters related to the flow of particulate matter between the two or more PM sensors 208. The one or more parameters can include any of: (i) a source of the aerosol flow based on a location of a first PM sensor 208 that detects an aerosol event, (ii) a path or direction of travel of the particulate flow based on a vector connecting the two or more PM sensors 208 that detect an aerosol event, and optionally, a decay in particle signal level between the first PM sensor that detects an aerosol event and each subsequent sensor that detects an aerosol event, (iii) a velocity of the aerosol flow based on a delay time between corresponding peaks in the two or more PM sensors 208 that detect an aerosol event, (iv) a decay of the aerosol flow based on a decrease in particle signal level between the first PM sensor that detects an aerosol event and each subsequent sensor that detects an aerosol event, and (v) one or more possible destinations of the aerosol flow based on the path of the aerosol flow, the decay of the aerosol flow, and / or the velocity of the aerosol flow.

[0081] In some examples, the air quality monitoring system 200 may comprise one or more additional sensors. The one or more additional sensors may comprise multiple additional sensors located at multiple locations within the monitoring area 202. In some examples, the one or more additional sensors may be co-located with each of the PM sensors 208. For example, each of the PM sensors 208 may form part of a sensor unit that comprises one or more additional sensors. The one or more additional sensors may comprise one or more of a carbon dioxide (CO2) sensor, a humidity sensor, a temperature sensor, and an air pressure sensor. As described above in connection with the first embodiment and further below, monitoring temperature, humidity, and / or pressure may help to (i) identify factors contributing to detected aerosol events and flows, and (ii) identify intervention measures to mitigate aerosol flows. Monitoring CO2 may help to (i) identify areas of stagnant air / poor ventilation, and (ii) identify the source of an aerosol event, whether it originates from human respiratory activity or mechanical activity (opening curtains, flapping bedsheets, etc.).

[0082] In some examples, the system 200 can include one or more outdoor sensors, e.g., sensors located outside a hospital building. The one or more outdoor sensors can include one or more PM sensors and / or one or more additional sensors. The outdoor sensors can monitor humidity, wind currents, temperature, PM counts, pressure, etc. The outdoor sensors allow the system 200 to take into account seasonal variations in particle levels. For example, temperature gradients between the outside and inside of a building can cause air currents that carry particulate matter. As a further example, background particle levels can change seasonally due to variations in pollen in the air.

[0083] In some examples, the air quality processing device 216 can output an intervention signal in response to determining the particle flow between the two or more PM sensors 208. The air quality processing device 216 can output the intervention signal to one or more (networked) air quality intervention mechanisms 222 via the communication network 218. In some examples, the air quality monitoring system 200 can include one or more air quality intervention mechanisms 222. In the example of FIG. 2, the one or more air quality intervention mechanisms include an automatic door 222 on the ward section 206.

[0084] The one or more air quality intervention mechanisms may include one or more of an automatic door or actuator thereof, an operating parameter of an air filtration device, an operating parameter of a heating, ventilation and air conditioning (HVAC) system, and a warning signal. The air quality system may output an intervention signal to activate (or cause to operate) the one or more air quality intervention mechanisms at a location associated with the source of the particulate matter flow (aerosol event), at a location associated with the path of the particulate matter flow, and / or at a location associated with one or more potential destinations of the particulate matter flow.

[0085] In some examples, the air quality system 200 can output an intervention signal to activate one or more automatic doors or other isolation means to isolate a particular area associated with the flow of particulate matter. In this manner, the air quality monitoring system 200 can isolate the flow of particulate matter to a restricted area, reducing the risk of airborne transmission of infectious particles.

[0086] In some examples, the air quality system 200 can output an intervention signal to activate an air filtration device 212 or adjust its operating parameters (e.g., fan speed) to increase air filtration and reduce particulate matter content. The system 200 can output an intervention signal to one or more air filtration devices 212 at the source of the particulate matter flow or along the path of the particulate matter flow. In this way, the detected particulate matter can be reduced, thereby reducing its further spread. The system 200 can output an intervention signal to one or more air filtration devices 212 at one or more predicted destinations of the particulate matter flow. In this way, the system 200 can take preventative measures to maximize air filtration in an area before the particulate matter flow arrives. The air filtration device 212 can comprise any of a ventilation system, an HVAC system, and an air cleaner. The air cleaner can comprise one or more of a HEPA filter, a carbon filter, and a UVC lamp.

[0087] In some examples, the air quality system 200 can output an intervention signal to activate an HVAC system or adjust operating parameters of the HVAC system. For example, the system 200 can output an intervention signal to adjust the temperature, ventilation, or humidity of an area associated with one or more of the PM sensors. In some examples, the system 200 can include temperature, humidity, pressure, and / or CO2 sensors co-located with each PM sensor 208. In this manner, the system 200 can determine areas associated with PM flow having temperature, humidity, pressure, and / or CO2 levels, or gradients thereof, above corresponding threshold levels. The intervention signal can adjust the HVAC system accordingly to reduce the areas of high temperature, humidity, pressure, or CO2 levels (or gradients thereof).

[0088] In some examples, the air quality system 200 can output an intervention signal to initiate an alert. The alert can include an audible and / or visual alarm signal, such as a siren or a flashing light. The alert can include an alert message or graphic on a computer system, or an informational signal, such as an email, text message, push notification, or other alert mechanism known in the art. The informational signal can include a graphical representation of the indoor setting to indicate the source, path, and / or potential destination of the PM flow. The graphical representation can be color coded to indicate the magnitude (and risk level) of the PM flow. The alert signal may alert one or more users (such as hospital staff) to the aerosol event, who can investigate the source of the alert and / or take corrective action.

[0089] In some examples, the air quality monitoring system 200 may include a memory for storing the particle level signals from each of the one or more PM sensors 208. The memory may also store data received from one or more additional sensors.

[0090] The air treatment device 216 may analyze the particle level signals for one or more PM sensors 208 over a period of time (e.g., an hour, a day, a week, or a month) to determine the presence of particulate matter in an area associated with one or more PM sensors 208. The presence of particulate matter may be related to the number or frequency of particulate matter flows or aerosol events associated with one or more PM sensors 208. The presence of a particular material may be related to the average particle level signal, the total time spent above an event threshold, or any other suitable particle level signal metric of the one or more PM sensors 208.

[0091] By determining particulate matter abundance of one or more PM sensors 208, the air quality monitoring system 200 can determine high-risk and low-risk areas of the healthcare environment 202 associated with relatively high or relatively low levels of aerosol events and / or particulate matter flow, respectively. For example, the air handling device 212 can determine high-risk areas of the healthcare environment 202 as areas having particulate matter prevalence above a first prevalence threshold, and similarly, low-risk areas of the healthcare environment as areas having particulate matter prevalence below a second prevalence threshold. The air handling device 216 can output data, such as reports, graphics, etc., indicating the high-risk and low-risk areas. As a result, a user can redesign the indoor setting accordingly. For example, low-risk areas (e.g., beds in alcoves or next to the air filtration device 212) can be designated to place high-risk infectious patients so that the flow of particulate matter that transmits infection is minimized. Correspondingly, high-risk areas associated with aerosol-generating events can be designated as suitable only for low-risk non-infectious patients. As a further example, additional intervention mechanisms can be identified to reduce the flow of particulate matter in high risk areas.

[0092] In some examples, the air handling device 212 can analyze the particle level signals of one or more PM sensors 208 over a period of time to determine periodic aerosol events associated with the one or more PM sensors 208. For example, the air handling device 212 can determine periodic occurrences (daily, weekly, etc.) of the same particulate matter flow and / or aerosol events. Such periodic events may be caused by routine activities in a hospital environment, which may be either ward rounds, visiting times, meal times, curtain opening, or other periodic events.

[0093] In some examples, the air handling device 212 can output data indicative of the periodic aerosol event, the time of occurrence of the periodic aerosol event, and / or the location of one or more PM sensors associated with the periodic aerosol event. In this manner, a user can correlate the periodic aerosol event with periodic activities or events and take appropriate remedial action, such as redesigning the area, preventing the periodic activity, or adjusting the process of the periodic activity to minimize the aerosol event (e.g., reducing the number of staff making ward rounds).

[0094] In some examples, the air handling device 212 can output an intervention signal to operate one or more intervention mechanisms in the vicinity of one or more PM sensors 208 associated with the periodic aerosol event at a time corresponding to the periodic aerosol event. The air handling device 212 can output an intervention signal to the one or more intervention mechanisms shortly before the occurrence of the periodic aerosol event. For example, the air handling device can increase the fan speed of an air filtration device and / or activate an automatic door to isolate the area.

[0095] In some examples, the air handling device 212 can receive operational data related to the monitored area 202. The air handling device 212 can receive operational data from a computer system associated with the monitored area, manual user input, and / or sensor input. The operational data can include details of operational or clinical events such as ward rounds, meal times, visiting times, patient admissions and discharges, patient and staff illnesses, etc. The air handling device 212 can correlate the operational details with aerosol events, periodic aerosol events, or particulate matter flows to identify aerosol event triggers so that the root cause of the event can be identified. A user and / or the air quality monitoring system 200 can then take corrective action and / or implement mitigation interventions as described above. In some examples, the air handling device 212 can implement an algorithm that correlates the operational details with aerosol events. The algorithm may be an artificial intelligence (AI) algorithm.

[0096] The air handler 212 / algorithm may perform some pre-processing on the particulate level signal before identifying an aerosol event, a periodic aerosol event, or a flow of particulate matter. The air handler 212 / algorithm may perform one or more of the following functions: Noise Removal Scaling ● Normalization - During a calibration period, the air treatment device can define normal values ​​for the particle level signal on a daily / weekly basis, etc. In this way, the algorithm can determine first and / or second event thresholds based on the normal values. Vectorization - The air handling device 212 can determine the flow of particulate matter based on a peak in a first particle level signal that exceeds a first event threshold at a first PM sensor at a first time and a corresponding peak in a second particle level signal that exceeds a second event threshold at a second PM sensor at a second time (shortly) after the first time. The air handling device 212 can define a "vector" of the flow of particulate matter from the first PM sensor to the second PM sensor. Periodic normalization - The air treatment device 212 can identify periodic anomalies / peaks that exceed a first event threshold to determine periodic aerosol events. ● Decision Trees / Heuristic Search / Specific Algorithms - The air handling device 212 may implement one or more measures to mitigate aerosol events, periodic aerosol events, and / or particulate matter flows, including: Outputting an intervention signal to remotely control one or more intervention mechanisms, such as activating an automatic door or increasing the fan speed of an air cleaner, following a detected aerosol event or at times corresponding to periodic aerosol events; o Outputting information to a user to take corrective measures. The user may be an operator who can adjust settings on an interventional mechanism to facilitate or increase efficiency at times corresponding to periodic aerosol events.

[0097] 2 is described with respect to determining the flow of particulate matter between two sensors, it will be understood that determining the flow of particulate matter is optional. In some examples, the system 200 can perform any of the interventions or data analysis and output described above based on the detection of an aerosol event as a particle level signal of one or more PM sensors 208 exceeding a first event threshold.

[0098] Figure 3 illustrates another air quality monitoring system 300 according to an embodiment of the present disclosure, and Figures 4-10d illustrate PM sensor data and analysis of aerosol events captured by air quality monitoring system 300. Features in Figure 3 that are also present in Figure 1 or Figure 2 have been given corresponding reference numbers in the 300 series and will not necessarily be described again here.

[0099] Air quality monitoring system 300 includes multiple PM sensors 308-a, 308-b, 308-c, and 308-d located within healthcare environment 302. A first PM sensor 308-a is located within ward section 306. A second PM sensor 308-b is located in hallway 304 adjacent to ward section 306. A third PM sensor 308-c is located in an alcove in hallway 304 across from ward section 306. A fourth PM sensor 308-d is located in hallway 304 on the opposite side of hallway door 322 from second PM sensor 308-b. System 300 otherwise has the same structural and functional characteristics as the system of FIG. 2, including an air handling device (not shown) and its associated functions.

[0100] An aerosol event occurred at bed 310 when a patient fell off bed 310 while sleeping at night.

[0101] 4 shows particle level signals 430-a, 430-b, 430-c, 430-d corresponding to PM1 concentrations (particle sizes between the lower detection limit and 1 μm) at each of the PM sensors 308 during a period surrounding an aerosol event. The horizontal axis corresponds to time in epochs, with each epoch equal to 5 seconds.

[0102] The first particle level signal 430-a corresponds to the first PM sensor 308-a in the ward section 306 and shows a large peak (off vertical scale) corresponding to an aerosol event. The second particle level signal 430-b corresponds to the second PM sensor 308-b in the hallway 304 and shows a peak that is delayed by about 5 minutes from the peak of the first particle level signal 430-a. The delay corresponds to the time it takes for the particulate matter flow to travel from the bed 310 to the second PM sensor 308-b. The third particle level signal 430-c corresponds to the third PM sensor 308-c in the alcove. A shallow peak is slightly visible around the same time as the peak of the second particle level signal 430-b. The fourth particle level signal 430-d corresponds to the fourth PM sensor 308-d in the hallway. A shallow peak is slightly visible around the same time as the peak of the second particle level signal 430-b.

[0103] FIG. 5 shows CO2 level signals 532-a, 532-b, 532-c, 532-d corresponding to the CO2 concentration at each of the PM sensors 308 (the system 300 also includes a CO2 sensor co-located with the PM sensor 308) during a period surrounding an aerosol event. A peak in the CO2 level can be seen in the first CO2 level signal 532-a corresponding to the CO2 sensor co-located with the first PM sensor 308-a. The rise in CO2 may be associated with the aerosol event. No corresponding peak is seen in the CO2 level signals 532-b, 532-c, 532-d corresponding to the CO2 sensors co-located with the PM sensors 308-b, 308-c, 308-d in the hallway 304. The data indicates that aerosol flow cannot be determined or tracked using CO2 sensors alone.

[0104] As outlined above, the air handling equipment of the system 312 can determine the flow of particulate matter between at least two PM sensors 308. One approach to this is to apply cross-correlation between related particle level signals. This can help to identify different particle level signal peaks that may not be visible to the naked eye (e.g., the third and fourth particle level signal peaks in FIG. 4 are difficult to identify). Figures 6a-9b show various cross-correlations between particle level signals 430a-d from PM sensors 308a-d over a period surrounding an aerosol event.

[0105] 6A shows the cross-correlation between the first PM1 particulate matter level signal from the first PM sensor 308-a and the second PM1 particulate matter level signal from the second PM sensor 308-b. A correlation peak (maximum r=0.836) is seen at a time lag of 74 epochs (370 seconds), indicating that PM1 particles are moving from the ward 306 to the hallway 304.

[0106] 6B shows the cross-correlation between the first PM10 particulate matter level signal from the first PM sensor 308-a and the second PM10 particulate matter level signal from the second PM sensor 308-b. A correlation peak (maximum r=0.836) is seen at a time lag of 74 epochs (370 seconds), indicating that PM10 particles are moving from the ward section 306 to the hallway 304.

[0107] FIG. 7A shows the cross-correlation between the first PM1 particulate matter level signal from the first PM sensor 308-a and the fourth PM1 particulate matter level signal from the fourth PM sensor 308-d. A correlation peak (maximum r=0.527) is seen at a time lag of 92 epochs (460 seconds). This indicates that PM1 particles are moving from the ward section 306 into the corridor 304 and then along the corridor 304. The correlation is not as strong as in FIG. 6A, indicating that the particulate matter flow decays as it progresses along the corridor 304. The time lag is larger reflecting the additional time to travel along the corridor 304.

[0108] FIG. 7B shows the cross-correlation between the first PM10 particulate matter level signal from the first PM sensor 308-a and the fourth PM10 particulate matter level signal from the fourth PM sensor 308-d. A correlation peak (maximum r=0.522) is seen at a time lag of 92 epochs (460 seconds). This indicates that PM10 particles are moving from the ward section 306 to the corridor 304 and then moving along the corridor 304. The correlation is not as strong as FIG. 6A, indicating a decay in particulate matter levels as the particulate matter moves along the corridor 304. The time lag is larger reflecting the additional time to travel along the corridor 304.

[0109] 8A shows the cross-correlation between the first PM1 particulate matter level signal from the first PM sensor 308-a and the third PM1 particulate matter level signal from the third PM sensor 308-c. A correlation peak (maximum r=0.580) is seen at a time lag of 65 epochs (325 seconds), indicating that PM1 particles are moving from the ward section 306 to the corridor 304.

[0110] 8B shows the cross-correlation between the first PM10 particulate matter level signal from the first PM sensor 308-a and the third PM10 particulate matter level signal from the third PM sensor 308-c. A correlation peak (maximum r=0.573) is seen at a time lag of 65 epochs (325 seconds), indicating that PM10 particles are moving from the ward section 306 to the hallway 304.

[0111] 9A shows the cross-correlation between the second PM1 particulate matter level signal from the second PM sensor 308-b and the third PM1 particulate matter level signal from the third PM sensor 308-c. A correlation peak (maximum r=0.649) is seen at a time lag of 6 epochs (30 seconds). This indicates that PM1 particles move along the corridor 304 and rise and fall at the second and third PM sensors 308-b, 308-c at approximately the same time.

[0112] 9B shows the cross-correlation between the second PM10 particulate matter level signal from the second PM sensor 308-b and the third PM10 particulate matter level signal from the third PM sensor 308-c. A correlation peak (maximum r=0.648) is seen at a time lag of 6 epochs (30 seconds). This indicates that PM10 particles move along the corridor 304 and rise and fall at the second and third PM sensors 308-b, 308-c at approximately the same time.

[0113] 10A-10D show cross-correlations between a first CO2 level signal from a CO2 sensor in the ward section 306 and first, second, third and fourth PM1 particle level signals corresponding to the first, second, third and fourth PM sensors 308-a, 308-b, 308-c, 308-d, respectively. All figures show correlation peaks between the CO2 levels in the ward section and the PM1 particulate matter level signals at each PM1 sensor. This is a complex relationship and difficult to interpret, but generally suggests that the CO2 levels in the ward section 306 rise before the PM1 levels (even in the ward section 306).

[0114] No correlation was found between the CO2 level signals 532-b, 532-c, 532-d in the hallway 304 and any other signals, confirming that CO2 sensors alone cannot be used to track particulate matter flow, especially when HVAC systems are present, which can very quickly dilute areas of high concentration.

[0115] The data in Figures 4-9d illustrate tracking of particulate matter flow in an indoor environment following an aerosol event. System 300 can respond by implementing intervention measures such as those described above (e.g., closing automatic door 322 before the particulate matter flow reaches it, or increasing the fan speed of an air cleaner). A user can evaluate the data output from system 300 to determine additional intervention measures or other remedial actions.

[0116] While the above discussion has been directed primarily to systems in medical environments, it will be understood that the present disclosure is not so limited and the disclosed systems apply to any indoor environment, including educational and commercial buildings.

[0117] It will be understood that any reference to "close to," "before," "slightly before," "after," "slightly after," "higher," or "lower," etc., can refer to a parameter of interest being less than or greater than a threshold value, or between two threshold values, depending on the context.

Claims

1. 1. An air quality monitoring system comprising: a plurality of particulate matter (PM) sensors positioned at corresponding locations within the monitoring area; and an air quality processing device coupled to each of the plurality of PM sensors via a communications network, the air quality processing device comprising: receiving particulate level signals from at least two of the plurality of PM sensors; and determining a flow of particulate matter between the at least two PM sensors based on the corresponding particulate level signals. equipped with an air quality treatment device; Air quality monitoring system.

2. The air quality treatment device comprises: detecting an aerosol event at a first PM sensor of the at least two PM sensors based on the particle level signal exceeding a first event threshold; and detecting the aerosol event at a second PM sensor of the at least two PM sensors based on the particle level signal exceeding a second event threshold.

2. The air quality monitoring system of claim 1, configured to determine a flow of particulate matter between the at least two PM sensors by:

3. The air quality monitoring system of claim 2 , wherein the first event threshold comprises an adaptive event threshold.

4. 4. The air quality monitoring system of claim 2 or 3, wherein the second event threshold comprises a scaled value of the first event threshold.

5. 3. The air quality monitoring system of claim 1 or 2, wherein the air quality processing device is configured to determine the particulate matter flow based on delay and / or amplitude difference between corresponding peaks of the particle level signal.

6. 3. The air quality monitoring system of claim 1 or 2, wherein the air quality processing device is configured to determine the particulate matter flow by applying cross-correlation to the particle level signals associated with the at least two PM sensors.

7. The air quality treatment device comprises: a source of said particulate matter stream; a flow path of the particulate matter; the velocity of the particulate matter flow; attenuation of the flow of particulate matter; and one or more predicted destinations for the particulate matter stream; The air quality monitoring system of claim 1 configured to identify one or more of:

8. The air quality monitoring system of claim 7 , wherein the air quality processing device is further configured to output an intervention signal configured to operate one or more air quality intervention mechanisms.

9. The air quality intervention mechanism comprises: Automatic doors or their actuators, Operating parameters of the air filtration device, Heating, ventilation and air conditioning (HVAC) system operating parameters; and Warning Signals 9. The air quality monitoring system of claim 8, comprising one or more of:

10. The air quality treatment device comprises: a location relative to the source of the particulate matter stream; a position relative to the path of the flow of particulate matter; and / or a location relative to the one or more potential destinations of the particulate matter stream; 10. An air quality monitoring system according to claim 8 or 9, configured to output the intervention signal to operate one or more air quality intervention mechanisms.

11. The air quality treatment device comprises: analyzing the particulate level signals for one or more PM sensors over a period of time to determine an abundance of particulate matter associated with the one or more PM sensors; The air quality monitoring system of claim 1 configured to output abundance data indicative of the abundance of particulate matter.

12. The abundance data is high-risk regions of the monitored area corresponding to one or more PM sensors having a particulate matter abundance above a first abundance threshold; and / or The air quality monitoring system of claim 11 , indicating low-risk regions of the monitored area corresponding to one or more PM sensors having a particulate matter abundance below a second abundance threshold.

13. the particulate matter abundance includes periodic aerosol events associated with the one or more PM sensors, and the abundance data comprises: the periodic aerosol events, the time of occurrence of the periodic aerosol events, and / or the location of the one or more PM sensors relative to the periodic aerosol event; 13. The air quality monitoring system of claim 11 or 12, wherein:

14. 14. The air quality monitoring system of claim 13, wherein the air quality processing device is configured to output an intervention signal for operating one or more intervention mechanisms at times corresponding to the periodic aerosol events.

15. The air quality treatment device comprises: receiving operational data relating to the monitored area; correlating one or more aerosol events with the operational data; The air quality monitoring system of claim 1 or 2 configured to identify an aerosol event trigger based on the correlation.

16. 3. The air quality monitoring system of claim 1, wherein each of the PM sensors is configured to measure the concentration of particulate matter in the air having particle sizes within a range from a lower detection limit to a particulate matter rating of the PM sensor.

17. 3. The air quality monitoring system of claim 1 or 2, wherein two or more of the PM sensors are positioned at different heights.

18. and a plurality of further sensors, the further sensors comprising: Carbon dioxide (CO2) sensor, Humidity sensor, temperature sensor, and Barometric pressure sensor 3. An air quality monitoring system according to claim 1 or 2, comprising one or more of:

19. 1. A method for monitoring a flow of particulate matter within a monitoring area, the method comprising: receiving a plurality of particulate level signals from at least two particulate matter (PM) sensors positioned within the monitored area; and determining a flow of particulate matter between the at least two PM sensors based on the corresponding particulate level signals.

20. 1. An air quality system for a healthcare environment, the air quality system comprising: a plurality of particulate matter (PM) sensors positioned at corresponding locations within the monitored area; and an air filtration device wirelessly coupled to the at least one PM sensor, the air filtration device comprising: receiving a particulate level signal from one or more of the plurality of PM sensors; an air filtration device configured to adjust a fan speed of the air filtration device in response to the particulate level signal; Air quality systems.

21. 21. The air quality system of claim 20, wherein the air filtering device is further configured to adjust the fan speed based on a distance between each of one or more of the plurality of PM sensors and the air filtering device.

22. 22. The air quality system of claim 21, wherein the air filtering device is configured to determine the distance between each of the one or more of the plurality of PM sensors and the air filtering device based on a received signal strength indicator of the particulate level signal.

23. 21. The air quality system of claim 20, wherein each of the PM sensors is configured to measure a concentration of particulate matter in the air having a particle size within a range from a lower detection limit to a particulate matter rating of the PM sensor.

24. 24. The air quality system of claim 23, wherein each PM sensor comprises a plurality of particulate matter ratings and is configured to measure a plurality of concentrations of particulate matter in the air within a corresponding plurality of particle size ranges.

25. each of the plurality of PM sensors is configured to measure a concentration of particulate matter in the air for a plurality of particle sizes; The air filtering device is a distance between each of the one or more PM sensors of the plurality of PM sensors and the air filtration device; and a concentration of particulate matter for each of the plurality of particle size ranges for each of the one or more PM sensors of the plurality of PM sensors; 25. The air quality system of claim 24 when dependent on claim 21 or 22, configured to adjust the fan speed based on:

26. The air filtering device is High Efficiency Particulate Air (HEPA) filters, Carbon filters, and UVC lamp 22. An air quality system according to claim 20 or 21, comprising an air purifier comprising one or more of:

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