Dynamic Simulation of Industrial Shock-Load Impacts on a Domestic-Design Wastewater Treatment Plant: A GPS-X Case Study of the Jenin Industrial Free Zone, Palestine

Abstract

The Jenin Industrial Free Zone Wastewater Treatment Plant (JIFZ-WWTP), an extended-aeration activated-sludge facility designed for domestic sewage, is required to serve an industrial catchment where unregulated effluents may enter without pretreatment. Because the plant was not yet operational, this study assessed its likely performance and shock-load vulnerability through dynamic simulation in GPS-X (v8.5, ASM1-based MANTIS), using a model calibrated on 728 laboratory measurements from a comparable operating plant (Jericho WWTP) and adapted to Jenin-specific conditions—an explicit methodological limitation. Under optimized steady state (RAS 460 m³/day, WAS 60 m³/day, capacity 1000 m³/day), the model indicated high simulated removals of TSS (95%), COD (90%) and cBOD₅ (94%) but limited total-nitrogen removal (34%); simulated effluent COD (109 mg/L) marginally exceeded the Class C reuse target. A 1000 m³/day untreated industrial shock load over ten days drove near-complete nitrification failure, secondary-clarifier sludge washout and severe effluent exceedances (COD ≈ 8000 mg/L). Monte Carlo analysis showed that only 33% of influent scenarios meet the COD and cBOD₅ limits simultaneously. Increasing the waste-activated-sludge rate was the most effective simulated recovery measure. These results, which require field validation once the plant is commissioned, indicate insufficient buffering against high-strength industrial inputs and support mandatory source pretreatment, continuous influent monitoring with early warning, and an anoxic stage with external carbon dosing.

1. Introduction

Wastewater treatment has evolved from a public-health-driven disposal objective toward an integrated approach that balances treatment efficiency and resource recovery, environmental sustainability, and economic feasibility [1,2]. In developing nations, where resources are often constrained, the selection of treatment technologies is particularly sensitive to cost, simplicity, and operational robustness [3,4]. Activated-sludge systems, especially extended-aeration configurations, remain the most widely adopted biological treatment method worldwide, accounting for over 90% of municipal wastewater treatment plant (WWTP) installations [5].

A growing concern in both developing and industrialized nations is the discharge of industrial effluents into treatment systems originally designed for domestic sewage. Industrial wastewater differs markedly from municipal sewage in terms of flow variability, chemical oxygen demand (COD) strength, nutrient ratios, and the presence of potentially inhibitory substances including heavy metals, surfactants, and other recalcitrant organic compounds [6,7]. The introduction of such effluents into activated-sludge systems can trigger a range of adverse effects: elevated food-to-microorganism (F/M) ratios, suppression of nitrifying bacteria, sludge deflocculation, secondary-clarifier overload, and, ultimately, effluent quality violations [8,9].

Mathematical modeling and process simulation have become established tools for evaluating WWTP performance, optimizing operating parameters, and anticipating system responses to unusual loading conditions before these materialize in practice [10,11]. Among the available simulation platforms, GPS-X (Hydromantis Environmental Software Solutions, Inc., Hamilton, ON, Canada) offers a well-validated environment integrating the Activated-Sludge Model No. 1 (ASM1) through its MANTIS framework, enabling the dynamic simulation of carbon oxidation, nitrification, denitrification, and sludge settling under a wide range of influent and operating conditions [12,13].

The Jenin Industrial Free Zone (JIFZ) represents a major infrastructure investment in the northern West Bank, Palestine, developed with German governmental support through the Palestinian Industrial Estates and Free Zones Authority (PIEFZA). The zone is designed to accommodate agro-food and light industrial operations across a 90-hectare footprint as shown in Figure 1, with a projected workforce of up to 5000 direct employees [14]. The JIFZ Wastewater Treatment Plant (JIFZ-WWTP), based on extended-aeration activated-sludge technology, was engineered for domestic-equivalent sewage at design flows of 1000–2000 m³/day. However, because the plant will serve an industrial park where individual factories are expected to treat their effluents to domestic equivalence before discharge, any failure of factory-level pretreatment units could expose the central WWTP to concentrated industrial loads for which it was not designed.

This operational vulnerability is further compounded by a complex external constraint unique to the Palestinian context: approximately 100 million ILS are annually deducted from Palestinian Authority tax revenues by Israel as a levy attributed to transboundary wastewater management costs [15]. This financial pressure reinforces the strategic importance of ensuring that the JIFZ-WWTP operates effectively and that effluent reuse for agricultural irrigation—particularly relevant in the fertile Marj Ibn Amer plain, which contributes approximately 16.2% of Palestine’s total agricultural output—is reliably maintained [11,14].

Despite the growing body of literature on GPS-X applications to municipal wastewater systems [16,17,18], relatively few studies have applied dynamic simulation specifically to assess the vulnerability of domestic-design WWTPs to industrial shock loading under conditions representative of developing-country industrial zones. Existing work on GPS-X calibration [19,20], Monte Carlo uncertainty analysis [21], and industrial pollutant permissibility thresholds [22] provides a useful methodological foundation, but site-specific applications that couple model calibration with probabilistic analysis and shock-load scenario testing remain scarce; this combination, rather than the geographic setting alone, constitutes the principal novelty claimed here.

The present study addresses these gaps by: (i) developing and calibrating a GPS-X dynamic model of the JIFZ-WWTP against operational data from a comparable facility; (ii) characterizing baseline treatment performance under optimized operating conditions; (iii) applying Monte Carlo sensitivity analysis to quantify performance variability under realistic ranges of influent industrial effluent quality; and (iv) simulating a worst-case shock-load event and tracing the biological, physical, and chemical consequences through the treatment train. Because the underlying model is calibrated on a surrogate plant rather than on JIFZ-WWTP itself, all findings are presented as model-based indications requiring field confirmation once the plant is operational, rather than as validated operational performance. The study is intended to provide evidence-based operational and design recommendations for the JIFZ-WWTP and to offer a transferable methodological framework for similar assessments in industrial zone contexts.

2. Materials and Methods

2.1. Study Site and System Configuration

The JIFZ-WWTP is located in the northeastern section of the Jenin Industrial Free Zone, Jenin Governorate, northern West Bank (approximately 32°27′ N, 35°17′ E; elevation +490 m AMSL). The plant employs extended-aeration activated-sludge technology and was designed for flows ranging from 1000 to 2000 m³/day at Stage 1 and Stage 2 of the zone’s phased development. The treatment train comprises an inlet works, two parallel aeration tanks (AT1 and AT2, each 1209 m³), two final sedimentation tanks (FST1 and FST2, each 168.7 m³), return-activated-sludge (RAS) and waste-activated-sludge (WAS) systems, and sludge-handling infrastructure. The process hydraulic retention time (HRT) is 29.07 h per aeration tank, and the design sludge retention time (SRT) is 20 days.

Design influent characteristics at Stage 1 include a BOD5 of 520 mg/L, COD of approximately 1040 mg/L, TSS of 520 mg/L, and TKN of 64 mg/L. Effluent quality targets for reuse in agricultural irrigation (Class C, Palestinian specifications) require a BOD5 ≤ 40 mg/L, COD ≤ 100 mg/L, TSS ≤ 50 mg/L, and TN ≤ 45 mg/L, along with nematode egg limits of ≤1 egg/L at Stage 2. Where a more stringent nitrogen figure (e.g., 10 mg/L TN) is referenced elsewhere in this manuscript for contextual comparison with other regulatory frameworks, this is explicitly noted as a non-operative benchmark; the Class C figure of ≤45 mg/L TN is the compliance criterion applied throughout this study. The Class C ammonia–nitrogen limit is a separate, more stringent criterion (NH₄-N ≤ 10 mg/L) and should not be conflated with the total-nitrogen limit (TN ≤ 45 mg/L); nitrite–N is reported without a limit because neither Palestinian standard sets one.

2.2. Simulation Platform and Model Framework

GPS-X version 8.5 (Hydromantis Environmental Software Solutions, Inc., Hamilton, ON, Canada), operated under an academic license, was used as the modeling environment. The activated-sludge process was configured using the MANTIS2 biological module, which extends the ASM1 framework to include aerobic denitrification, heterotrophic and autotrophic growth kinetics with Monod-type expressions, and substrate storage dynamics. The secondary clarification process was represented using the Simple1d (Noreact1d) one-dimensional flux model, which captures the vertical solids concentration profile, sludge blanket dynamics, and RAS extraction behavior under variable loading.

The model structure as shown in Figure 2 comprised over 60 composite and state variables, supported by expression libraries covering more than 30 stoichiometric and 24 kinetic parameters. Key modeled processes included aerobic oxidation of soluble and particulate COD, nitrification by ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB), anoxic denitrification, endogenous respiration, and biological phosphorus cycling.

2.3. Model Calibration

Because the JIFZ-WWTP was not yet fully operational at the time of this study, model calibration was performed using data from the Jericho Wastewater Treatment Plant (Jericho WWTP), which employs the same extended-aeration activated-sludge technology under comparable Palestinian operating conditions. Calibration data comprised 728 laboratory measurements conducted in November 2024, encompassing influent and effluent concentrations for COD, BOD₅, TSS, TN, NH₄-N, and PO₄-P across a full operational month.

The calibration strategy followed a sequential, parameter-by-parameter approach aligned with the IWA Good Modelling Practice (GMP) guidelines [23], structured in five explicit steps: (1) influent characterization and fractionation—total COD was partitioned into its soluble inert, particulate inert, readily biodegradable, and slowly biodegradable fractions using the respirometry and stoichiometric relationships embedded in the MANTIS2 framework; (2) mixed liquor suspended solids (MLSSs) matching—achieved by adjusting the volatile fraction of the influent TSS (VSS/TSS ratio, calibrated from 0.75 to 0.61) and the particulate inert COD fraction (reduced from 0.13 to 0.055); (3) dissolved oxygen (DO) concentration calibration—achieved by adjusting the alpha factor for fine-bubble aeration (set at 0.7) and accounting for site-specific oxygen saturation at the calibrated elevation and an operating temperature of 25.1 °C; (4) sludge production adjustment—verified by matching observed effluent TSS against simulated values, with the sludge volume index (SVI) calibrated at 189.5 mL/g; and (5) effluent quality verification across all monitored parameters. This structured sequence ensured that each subset of parameters was fixed before the next was adjusted, preventing compensatory errors between interdependent variables.

The use of Jericho WWTP data as a surrogate-calibration dataset warrants explicit justification and a transparent acknowledgement of the uncertainty it introduces. Both plants employ identical extended-aeration activated-sludge technology with comparable hydraulic retention times (≥24 h) and sludge retention times (≥20 days), and both operate under Palestinian Authority regulatory and operational norms. Climatically, Jericho and Jenin share a semi-arid Mediterranean profile, with similar summer temperatures (25–32 °C) and comparable seasonal rainfall patterns, though Jericho’s lower elevation (−258 m AMSL, below sea level, versus +490 m AMSL for JIFZ) produces slightly different oxygen saturation values, a difference corrected for in the model. The primary source of uncertainty introduced by surrogate calibration lies in influent composition: Jericho WWTP receives predominantly domestic sewage, whereas the JIFZ-WWTP is designed to receive industrial-equivalent effluents from agro-food and light manufacturing operations. To account for this, the readily biodegradable COD fraction was adjusted upward from the Jericho-calibrated value of 0.2 to 0.7, and TKN and total phosphorus concentrations were re-estimated from PIEFZA technical documentation rather than from Jericho influent measurements.

A structured comparison of the two facilities is summarized in

Table 1. Jericho WWTP vs. JIFZ-WWTP.

Attribute	Jericho WWTP (Surrogate)	JIFZ-WWTP (Target)
Technology	Extended-aeration activated sludge	Extended-aeration activated sludge
HRT	32 h	29.07 h
SRT	23 d	20 d (design)
Aeration system	diffusers (bottom-mounted) with blowers, DO/ORP control, submersible mixers	diffusers (bottom-mounted) with blowers, DO/ORP control, submersible mixers
Reactor configuration	2 × 2050 m3 aeration tanks	2 × 1209 m3 aeration tanks
Clarifier geometry	Sloping-bottom; sidewall 6.4 m; area 320.31 m2	Sloping-bottom; sidewall 5.63 m; area 214.74 m2
Elevation (AMSL)	−258 m	+490 m
Operating temperature	28.2 °C (measured mean; 25.1 °C used as the simulation setpoint)	25.1 °C
Industrial influent contribution	Agro-food/light industrial (design basis)	Agro-food/light industrial (design basis)
Monitoring data available	728 measurements, Nov 2024	None (pre-commissioning)

.

While this transfer approach introduces parametric uncertainty—particularly in kinetic constants governing nitrification and denitrification—it represents the most defensible option available prior to plant commissioning. To make this uncertainty explicit rather than implicit, an approximate error envelope is estimated below.

Approximate propagated uncertainty: because effluent COD, cBOD5 and TN are most sensitive to the readily biodegradable COD fraction and to TKN/TP inputs—the two parameter groups re-estimated from PIEFZA documentation rather than transferred directly from Jericho—the Monte Carlo ranges applied to these inputs (Section 2.4) are treated in this study as a first-order proxy for surrogate-calibration uncertainty, rather than as representing measurement or sampling variability alone. Under this framing, the compliance probabilities reported in Section 3.3 (e.g., 33% joint COD/cBOD5 compliance) should be read as reflecting combined influent-variability and surrogate-transfer uncertainty, and the true confidence interval on any single point estimate (e.g., baseline COD = 109.2 mg/L) is wider than a calibration-only analysis would suggest.

Note on units: concentrations expressed in mg/L are numerically equivalent to g/m³ (i.e., 1 mg/L = 1 g/m³ for dilute aqueous solutions); gCOD/m³, gN/m³ and gP/m³ values reported for GPS-X model variables are therefore directly comparable to the corresponding mg/L concentrations reported elsewhere in the text and tables.

Key calibrated parameters included total COD (increased from 430 to 981 gCOD/m³), total TKN (adjusted from 40 to 57.4 gN/m³), total phosphorus (modified from 10.0 to 17.3 gP/m³), and the readily biodegradable fraction of total COD (increased from the Jericho-fitted value of 0.2 to an assumed 0.7 for the JIFZ industrial catchment; the latter is a design assumption, not a calibrated value), reflecting the elevated organic and nutrient strengths of Palestinian industrial wastewater. Site-specific adjustments were applied for operating temperature (25.1 °C), elevation (affecting oxygen solubility), and clarifier geometry (sloping-bottom design, sidewall depth 5.63 m, area 214.74 m²). The alpha factor for fine-bubble aeration was set at 0.7, and the sludge volume index (SVI) was calibrated at 189.5 mL/g.

Calibration performance was assessed by comparing simulated effluent concentrations against measured values from Jericho WWTP. The calibrated model yielded an effluent TSS of 17.2 mg/L, COD of 33.6 mg/L, BOD₅ of 14.8 mg/L, and ammonia–N of 0.119 mg/L, which fall within acceptable ranges relative to observed values. The wave-like (oscillatory) pattern visible in the calibrated effluent traces (Figure 3) reflects the diurnal dynamic influent pattern used to drive the simulation: the daily load cycle propagates to the effluent in attenuated form, damped by the hydraulic retention time and the biological buffering of the extended-aeration reactor, and it stabilizes as the model approaches steady state. The calibrated model was subsequently adapted to JIFZ-WWTP by incorporating No independent validation dataset exists: the November 2024 Jericho record was used in full for calibration, and the JIFZ-WWTP was not yet commissioned, so the adapted model has not been validated against observed plant performance (

Table 2. Jericho WWTP calibration dataset, November 2024: mean (range) and number of analyses per determinant.

Analyte	Influent Mean (Range), n	Effluent Mean (Range), n
COD (mg/L)	967 (780–1320), n = 10	26.7 (12–35), n = 9
BOD5 (mg/L)	482.5 (450–520), n = 10	10.3 (8–12), n = 4
TSS (mg/L)	414 (300–490), n = 9	9.3 (6–15), n = 10
NH4-N (mg/L)	51 (36.5–66), n = 9	0.95 (0.27–1.35), n = 11
Total N (mg/L)	58.6 (43.2–72), n = 11	4.7 (1–11), n = 13
PO4-P (mg/L)	19 (8.76–33), n = 12	1.5 (0.2–3), n = 12
Temperature (°C)	28.2 (25.0–29.9), n = 20	27.7 (25.1–29.6), n = 20
pH	7.1 (6.89–7.40), n = 20	6.9 (6.6–7.3), n = 20

). As

Table 3. Observed versus simulated effluent quality at Jericho WWTP after calibration.

Effluent Analyte	Measured (Nov. 2024)	Simulated	Deviation
COD (mg/L)	26.7	33.6	+6.9 (+26%)
cBOD5 (mg/L)	10.3	14.8	+4.5 (+44%)
TSS (mg/L)	9.3	17.2	+7.9 (+85%)
NH4-N (mg/L)	0.95	0.119	−0.83 (−87%)

shows, the calibrated model reproduces effluent COD within the observed monthly range but over-predicts effluent solids and under-predicts effluent ammonia; this residual bias is carried forward as a limitation rather than presented as agreement.

Table 4. Provenance of model inputs: measured, fitted, transferred and assumed.

Category	Examples	Status
Measured at Jericho	Influent/effluent COD, BOD5, TSS, NH4-N, TN, PO4-P, temperature, pH (Table 2)	Observation
Fitted to Jericho data	VSS/TSS 0.75 to 0.61; particulate inert COD 0.13 to 0.055; alpha = 0.7; SVI 189.5 mL/g; rbCOD fraction 0.2	Calibrated
Transferred unchanged	MANTIS2 default kinetic and stoichiometric coefficients not listed as fitted	Transferred
JIFZ design assumption	Influent COD 1040; TKN 64; TP 6.0; rbCOD fraction 0.7; tank geometry; design flow 1000 m3/d	Assumption

summarizes the provenance of all model inputs, distinguishing values measured at Jericho WWTP, parameters fitted during calibration, MANTIS2 coefficients transferred unchanged, and design assumptions adopted for JIFZ-WWTP. The plant’s specific tank geometry, design flow rates, and influent composition were estimated from PIEFZA technical documentation.

All simulations used the GPS-X MANTIS2 biological library, which extends ASM1 with explicit phosphate-accumulating-organism (PAO) kinetics and chemical-precipitation pathways; enhanced biological phosphorus removal is therefore represented, unlike in unmodified ASM1. The near-complete simulated orthophosphate removal (99.88%) arises from two co-active mechanisms: biological uptake by PAOs (PHA storage/release) and mineral precipitation. The MANTIS2 aeration-tank rate outputs (Appendices E and F, before and during the shock load) show non-zero calcium carbonate and struvite precipitation rates, with only minor AlPO₄/FePO₄ contributions, confirming that these precipitation processes were active. Because this efficiency is high relative to biological-only removal, it should be validated against measured JIFZ effluent phosphorus once the plant is commissioned.

Calibration performance was assessed by comparing simulated effluent concentrations against measured values from Jericho WWTP. The calibrated model yielded an effluent TSS of 17.2 mg/L, COD of 33.6 mg/L, BOD5 of 14.8 mg/L, and ammonia–N of 0.119 mg/L, which fall within acceptable ranges relative to observed values.

The calibrated model was subsequently adapted to JIFZ-WWTP by incorporating the plant’s specific tank geometry, design flow rates, and influent composition estimated from PIEFZA technical documentation. For clarity, three distinct modeling steps are therefore involved and are kept terminologically distinct throughout this manuscript: (i) calibration of influent characteristics and kinetic and settling parameters against Jericho WWTP data (

Table 5. Influent calibrated characteristics.

Variable	Unit	Default Value in GPS-X	Calibrated Value
total COD	gCOD/m3	430.0	981.0
total TKN	gN/m3	40.0	57.4
total phosphorus	gP/m3	10.0	17.3
orthophosphate	gP/m3	8.0	3.0
VSS/TSS ratio	gVSS/gTSS	0.75	0.61
readily biodegradable fraction of total COD	-	0.2	0.7
particulate inert fraction of total COD	-	0.13	0.055
colloidal fraction of slowly biodegradable COD	-	0.15	0.1
P content of soluble inert material	gP/gCOD	0.01	0.02
pH	-	7.0	7.4

and

Table 6. Calibrated FST parameters.

Variable	Unit	Value
maximum settling velocity	m/d	315.457
hindered zone settling parameter	m3/gTSS	0.00044323
flocculant zone settling parameter	m3/gTSS	0.00275097
rate constant for storage of PHA	gCOD/gPAO/d	6.0
saturation coefficient of PAO for Sac	mgCOD/L	4.0
saturation coefficient for Xpp/Xbp	gP/gCOD	0.01
maximum growth rate of PAO	1/d	1.0
aerobic decay coefficient for PAO	1/d	0.2

); (ii) transfer/adaptation of the calibrated parameter set to JIFZ-specific geometry and estimated influent; and (iii) scenario simulation (baseline, Monte Carlo, shock-load) using the adapted JIFZ model. The resulting model is referred to as an ‘adapted’ or ‘transferred’ model of JIFZ-WWTP rather than a fully calibrated model of JIFZ-WWTP itself.

2.4. Simulation Scenarios

Three primary simulation scenarios were examined:

(1) Baseline steady-state performance: The JIFZ-WWTP was operated at the design influent composition and at the optimal RAS/WAS setpoints determined through parametric optimization. Key performance indicators (removal efficiencies for TSS, VSS, COD, cBOD₅, TN, TKN, NH₃-N, S-PO₄, and TP) were recorded.

(2) Monte Carlo probabilistic analysis: To quantify performance variability under realistically uncertain influent conditions, 1000 Monte Carlo simulation runs were executed. Four input parameters were assigned uniform probability distributions: total COD (800–1500 mg/L), TKN (40–80 mg/L), readily biodegradable COD fraction (0.4–0.8), and orthophosphate (1–5 mg/L). The four inputs were sampled independently, with no imposed correlations. This is therefore a four-parameter screening exercise rather than a sensitivity analysis: because run-level records were not retained, the drivers of non-compliance cannot be ranked and surrogate-calibration uncertainty cannot be propagated, so the compliance frequencies reported below should be read as indicative only. Compliance probabilities were computed for effluent TSS (<50 mg/L), cBOD5 (<40 mg/L), COD (<100 mg/L), and TN (<45 mg/L). The uniform distribution was selected as a conservative, minimally informative choice in the absence of a measured JIFZ influent distribution; it assigns equal probability mass across the full plausible range rather than concentrating probability around an assumed central estimate, which would not be justifiable without operational data. This choice is acknowledged as a simplification: uniform distributions do not reflect the more centrally weighted variability typically observed in real industrial discharge records.

All dynamic simulations were initialized from steady state: the adapted JIFZ-WWTP model was first run to equilibrium, and the resulting state served as the starting condition for each scenario. Dynamic runs used an adaptive Runge–Kutta solver with a base integration time step of 0.1–0.25 h. A stabilization period of 3–5 days preceded each shock-load scenario to ensure a stable operating point before perturbation. The shock was introduced as a step increase of 1000 m³/day of untreated industrial flow on Day 3, sustained for 10 days, using time-series influent data derived from the PIEFZA estimates and literature values for the mixed industrial streams.

The four influent variables were sampled from independent uniform distributions with no imposed inter-parameter correlations. GPS-X returned only the aggregate compliance frequencies rather than a per-realization input–output log; consequently, a formal sensitivity ranking of the drivers of non-compliance could not be computed for this version and is identified as a priority for future work, in which the runs will be scripted so that each realization’s inputs and outputs are retained.

(3) Industrial shock-load scenario: A sudden discharge of 1000 m³/day of untreated industrial effluent, representative of the blended output of the five industrial sectors (

Table 7. Characterization of the untreated industrial streams used in the shock-load scenario (concentrations in mg/L; each stream enters at 200 m³/day, together replacing the 1000 m³/day baseline flow).

Stream	COD	Sol. COD	cBOD5	TSS	TKN	Ortho-P	TP	NH4-N	pH
Textile	990	765	196	236	54	7.9	7.9	32	7.0
Meat	7230	5541	3089	2149	250	7.9	10.0	25	7.0
Dairy	1745	1080	308	718	75	7.9	9.1	41	7.0
Tannery	4180	1414	1444	3238	250	7.9	10.0	25	7.0
Energy drink	33,000	26,891	14,317	6229	54	0	2.5	25	5.4
Domestic (ref.)	1040	800	642	285	64	3.0	6.0	25	7.4

), and, separately, (4) a high-strength variant in which the influent is dominated by energy-drink processing effluent, reported in Section 3.6; the two differ by influent composition rather than by flow, and are distinguished throughout the Results, of combined output from energy drink manufacturing, meat and dairy processing, textile, and tannery industries present in the JIFZ, was superimposed on the normal influent stream on Day 3, persisting for ten days (Days 3–13). The shock load replaced the baseline influent flow, so total plant throughput remained approximately 1000 m³/day during the event; this is confirmed by the secondary-clarifier flow balance reported in Section 3.5. The effluent quality trajectory, microbial population dynamics, clarifier behavior, and recovery kinetics were tracked over the simulation period.

In addition to the primary worst-case shock-load scenario (1000 m³/day of untreated industrial effluent over ten days), two supplementary scenarios were simulated to define operational thresholds and improve the practical relevance of the findings for plant operators and regulators: (a) a moderate shock load of 500 m³/day of industrial effluent over five days, representing a partial pretreatment failure or a single-factory discharge event; (b) a brief high-intensity shock of 1000 m³/day over three days, simulating an emergency bypass or accidental spill of short duration. These scenarios were designed to identify the threshold conditions under which the JIFZ-WWTP retains the capacity for self-recovery without operator intervention, and to distinguish transient disturbances from events that require active emergency management. All shock-load results should be interpreted as worst-case stress tests of plausible upper-bound industrial inputs rather than as predictions of routine operating conditions.

2.5. RAS and WAS Optimization

The design RAS and WAS rates of 320 m³/day and 50 m³/day, respectively, were subjected to systematic optimization. Iterative simulations were conducted across a matrix of RAS rates (200–600 m³/day) and WAS rates (40–100 m³/day) at a fixed influent flow of 1000 m³/day. The optimization criterion was the maintenance of effluent quality within Class C reuse standards across all key parameters simultaneously. The optimal operating setpoints were identified as RAS = 460 m³/day and WAS = 60 m³/day.

3. Results and Discussion

Because the underlying model was calibrated on Jericho WWTP data and adapted to JIFZ-WWTP prior to commissioning (Section 2.3), all results below are model-based indications of expected performance rather than validated operational data.

3.1. Baseline Treatment Performance Under Steady-State Conditions

Under optimized steady-state simulation, the model indicates high pollutant removal efficiencies across most parameters, as detailed in

Table 8. Simulated removal efficiencies of key pollutants under steady-state conditions at optimal RAS (460 m³/day) and WAS (60 m³/day) setpoints. Compliance is evaluated against the Class C agricultural reuse standard (Section 2.1).

Parameter	Influent (mg/L)	Effluent (mg/L)	Removal Efficiency (%)	Class C Reuse Target	Compliant?
TSS	284.7	13.72	95.18	≤50 mg/L	Yes
VSS	173.7	11.09	93.62	n/a	n/a
COD	1040	109.2	89.50	≤100 mg/L	No—marginal exceedance
cBOD5	642.4	36.51	94.32	≤40 mg/L	Yes
Ammonia–N	25	0.33	98.70	n/a	n/a
TKN	64	4.82	92.50	n/a	n/a
TN	64	42.39	33.80	≤45 mg/L	Yes
S-PO4	3	0.0037	99.88	n/a	n/a
TP	4	1.342	66.45	n/a	n/a

Note: n/a = not applicable; no limit is specified for this parameter in the Class C agricultural reuse standard (Section 2.1), so compliance was not assessed.

. As these results derive from the adapted simulation model rather than from measured JIFZ-WWTP operation, they are reported here as simulated performance.

The high removal efficiencies for TSS (95.18%) and VSS (93.62%) reflect the effectiveness of biological flocculation and secondary settling within the extended-aeration configuration, which provides an HRT of 29.07 h and an SRT of 20 days—both consistent with extended-aeration system design criteria (HRT: 18–36 h; SRT: 20–40 days) [5,24]. The cBOD₅ removal of 94.32% confirms that the prolonged solids retention time supports complete carbonaceous oxidation with negligible accumulation of biodegradable organics in the effluent.

COD removal at 89.5% was slightly lower than cBOD5, which is a characteristic feature of extended-aeration systems treating mixed industrial-domestic influents, where a portion of the total COD comprises slowly biodegradable or non-biodegradable compounds that resist biological degradation within the available HRT. This same mechanism explains why simulated effluent COD (109.2 mg/L) marginally exceeds the 100 mg/L Class C reuse target even though cBOD5 comfortably meets its own target: the residual COD fraction is disproportionately composed of slowly biodegradable/inert material not captured by the cBOD5 test. This indicates that, on current design assumptions, an additional polishing step (e.g., filtration or extended-aeration time) may be required to reliably achieve full Class C COD compliance, and this has been reflected in the Conclusions.

The high ammonia–N removal (98.7%) and TKN removal (92.5%) indicate that nitrification was proceeding effectively, consistent with the model’s autotrophic growth kinetics at 25.1 °C and adequate dissolved oxygen supply.

Total-nitrogen removal of 33.8% remains below the more stringent aspirational TN target referenced elsewhere for comparison with other regulatory frameworks (Section 2.1), though it does meet the ≤45 mg/L Class C TN standard applied in this study (42.39 mg/L simulated). The effluent TN concentration of 42.39 mg/L is dominated by nitrate–N (approximately 37.31 mg/L), thereby confirming that nitrification was essentially complete but that denitrification was severely limited by the absence of a dedicated anoxic stage and by low carbon availability in the latter stages of the aeration train. This finding is consistent with the broader literature on extended-aeration systems, which generally achieve TN removal of 30–60% without structural modification [5,25]. Targeted improvements, including the introduction of an anoxic reactor, external carbon dosing (e.g., methanol or acetate), and optimization of the SRT toward the upper design range, are recommended to address this deficit.

Orthophosphate (S-PO₄) removal was near-complete at 99.88% in the simulation. As no chemical dosing system is configured in the baseline scenario, this removal is attributed in the model to a combination of biological assimilation into biomass and modeled mineral precipitation pathways (Section 2.2). Because this removal efficiency is very high relative to typical biological-only phosphorus removal performance, it should be treated cautiously until validated against measured JIFZ effluent phosphorus data; if the operational plant does not achieve comparable removal once commissioned, the precipitation kinetics in the calibrated model parameter set (Table 5) would be the first item to revisit.

Total phosphorus removal of 66.45% was constrained by the particulate phosphorus fraction, which is less readily captured by biological processes alone.

These baseline performance results are broadly consistent with published data from comparable extended-aeration systems receiving mixed industrial-domestic influents. Elawwad et al. [7], modeling a full-scale municipal-industrial WWTP in Egypt using ASM3, reported COD removal efficiencies of 85–92% and nitrification efficiencies exceeding 90% under stable loading, broadly matching the JIFZ-WWTP’s simulated values of 89.5% and 98.7%, respectively, though the comparison remains indicative rather than validating, given the surrogate-calibration basis of the present model. Similarly, Collivignarelli et al. [9], assessing the impact of a new industrial discharge on an Italian urban WWTP, observed that extended-aeration configurations maintained TSS removal above 90% under moderate industrial loading, consistent with the 95.18% removal simulated here. The comparatively low TN removal of 33.8%, attributable to the absence of a dedicated anoxic stage, is likewise consistent with the broader literature: Henze et al. [25] established that conventional single-sludge extended-aeration systems without structured anoxic zones typically achieve TN removals of 20–40%, reinforcing the recommendation for anoxic-stage retrofitting.

In summary, the adapted JIFZ-WWTP model indicates effective performance under optimized steady-state conditions for most regulatory parameters, with total-nitrogen removal and marginal COD compliance representing the principal performance gaps requiring structural intervention and/or field verification.

3.2. Parametric Optimization of RAS and WAS Flows

The design RAS rate of 320 m³/day proved insufficient to maintain adequate biomass recirculation at influent flows approaching 1000 m³/day. Systematic optimization identified RAS = 460 m³/day and WAS = 60 m³/day as the optimal setpoints as shown in Figure 4. At these values, the JIFZ-WWTP successfully managed a maximum influent flow of 1000 m³/day without exceeding effluent quality thresholds for Class C agricultural reuse. The increased RAS rate maintains the MLSS concentration in the aeration tanks at levels sufficient to sustain both heterotrophic and autotrophic activity, while the WAS rate of 60 m³/day controls sludge age and prevents excessive accumulation of inert volatile solids.

In contrast, the unoptimized design setpoints constrained the maximum treatable flow to approximately 900 m³/day before effluent COD and TSS limits were breached. RAS/WAS optimization improved simulated effluent quality, but no setpoint achieved simultaneous compliance with all Class C limits at 1000 m³/day; the selected operating point is therefore reported as a trade-off rather than as a compliant optimum.

In summary, RAS/WAS optimization increased the effective treatment capacity by approximately 11%, demonstrating that significant operational gains are achievable through parametric tuning without capital investment.

3.3. Monte Carlo Probabilistic Analysis

Monte Carlo simulations across 1000 realizations of uncertain influent conditions revealed that treatment compliance was highly variable, particularly for nitrogen and organic parameters as shown in Figure 5. TSS compliance (<50 mg/L) was achieved in 87% of scenarios, reflecting the system’s robust solids-capture capability across a wide range of influent TSS concentrations. In contrast, only 68% of simulations met the TN target of <45 mg/L, and just 33% satisfied both the COD (<100 mg/L) and cBOD₅ (<40 mg/L) standards simultaneously.

These results underscore the fundamental sensitivity of the JIFZ-WWTP to industrial discharge variability. The wide range of readily biodegradable COD fraction (0.4–0.8) was particularly influential: at the lower end of this range, the heterotrophic biomass is starved of rapidly available substrate for nitrification support, while at the upper end, the high F/M ratio can temporarily outpace the oxygen transfer capacity, leading to dissolved oxygen deficits that impair nitrification.

The Monte Carlo outcomes are consistent with similar probabilistic assessments of extended-aeration WWTPs receiving variable industrial inputs [21,22], and they quantify the operational risk more concretely than deterministic single-scenario modeling alone. As noted in Section 2.4, a sensitivity check using triangular or truncated-normal distributions in place of the uniform assumption would help establish how robust these compliance percentages are to the distributional assumption itself.

In summary, probabilistic analysis confirms that the JIFZ-WWTP’s compliance with COD and organic standards is highly sensitive to industrial discharge variability, with only one-third of realistic scenarios meeting organic effluent targets, a finding that strongly supports mandatory influent pre-screening and real-time monitoring, while also underscoring that this conclusion should be re-checked against the sensitivity analyses requested above before it is treated as a robust design basis.

3.4. Shock-Load Event: Effluent Quality Dynamics

The simulation of an uncontrolled industrial discharge of 1000 m³/day over Days 3–13 produced severe treatment exceedances across all monitored parameters in the model. Peak effluent concentrations during the shock period are summarized in

Table 9. Simulated peak effluent concentrations during the shock-load event (Days 3–13) compared with Palestinian permissible discharge limits.

Parameter	Pre-Shock Effluent (mg/L)	Peak During Shock (mg/L)	Palestinian Discharge Limit (mg/L)
COD	109.2	~8000	≤250
BOD5	36.5	~3500	≤50
TSS	13.7	~510	≤50
TKN	4.8	~37	≤50
Total Phosphorus	1.3	~63	≤5
Nitrite–N	0.26	~81.9	-

, which reports simulated peak effluent concentrations at the plant outlet and applies the Palestinian discharge limit, a separate, less stringent standard than the Class C reuse target used in Table 8.

These peak values are from the blended industrial shock scenario shown in Figure 6. The higher-strength scenario, in which the influent is dominated by energy-drink processing effluent, produces substantially larger excursions and is discussed separately in Section 3.6; headline figures quoted elsewhere in this paper refer to the blended scenario unless stated otherwise.

The immediate cause of these exceedances was an order-of-magnitude increase in the organic loading rate. The F/M ratio rose from a stable pre-shock value of approximately 0.33 kgBOD₅/(kgMLVSS·day) to 1.60 kgBOD₅/(kgMLVSS·day), representing a near-fivefold overload. Aerobic hydrolysis of particulate and colloidal COD accelerated approximately fourfold, and the net accumulation of readily biodegradable COD in the aeration tank became positive (+258 mg COD/L·day), indicating that the substrate was being generated by hydrolysis faster than it could be consumed by heterotrophic oxidation.

Nitrification collapsed almost entirely during the shock period in the simulation. Under the high-strength scenario (Section 3.6), the failure is more pronounced: the aeration-tank oxygen uptake rate falls from 345 to 51 mg O2/(L.d), heterotrophic biomass from 682 to 24 mg COD/L, ammonia-oxidizer biomass from 16.9 to 2.8 mg COD/L and nitrite-oxidizer biomass from 4.5 to 0.7 mg COD/L, while the inactive fraction of the volatile suspended solids approaches 100 per cent. The nitrification rate declined from 1.05 mgN/(L.h) to less than 0.001 mgN/(L.h), and the nitrite accumulation rate increased fivefold, reflecting the competitive displacement of slow-growing nitrifiers by rapidly proliferating heterotrophs that outcompeted them for dissolved oxygen and available space. Ammonia-oxidizing bacteria (AOB) net growth remained marginally positive in the model, suggesting that the impact was primarily one of substrate competition and oxygen stress rather than acute toxicity toward AOB specifically, a distinction of considerable operational importance, since it implies that nitrifier populations could recover without re-seeding once the shock abates, provided the shock load itself does not carry additional inhibitory compounds not represented in this model (see Section 3.6 for the organic overload vs. toxicity discussion).

Phosphate-accumulating organisms (PAOs) were effectively inactivated during the shock event, with PHA storage rates declining by two orders of magnitude. Biological phosphorus removal therefore ceased, and phosphate removal shifted to physicochemical pathways: calcium carbonate precipitation increased 860-fold and struvite precipitation rose approximately 100-fold, driven by CO₂ stripping-induced pH elevation (from 7.73 to 8.46) and by the high concentrations of calcium, magnesium, ammonium, and phosphate in the mixed liquor as shown in Figure 6, Figure 7 and Figure 8.

• Operational Thresholds: Moderate and Short-Duration Shock Scenarios

The supplementary shock-load simulations revealed a clear threshold behavior in system response. Under the moderate scenario (500 m³/day over five days), the F/M ratio rose to approximately 0.85 kgBOD₅/(kgMLVSS·day)—elevated but within the range from which extended-aeration systems can partially self-recover. Effluent COD peaked at approximately 2100 mg/L and TSS at approximately 180 mg/L, both exceeding Palestinian permissible limits but recovering to near-baseline levels within seven days of the shock subsiding without operator intervention. Nitrification was suppressed but not eliminated, with the ammonia effluent concentration rising to approximately 12 mg/L before declining. The clarifier remained operationally stable, with the sludge blanket rising but not breaching the effluent weir.

Under the brief high-intensity scenario (1000 m³/day over three days), effluent quality deteriorated rapidly—COD peaked at approximately 5200 mg/L and TSS at approximately 310 mg/L—but the shorter exposure duration limited irreversible biological damage. Nitrification collapsed for approximately four days post-event but recovered without elevated WAS intervention within ten days, indicating that the nitrifier population, while severely suppressed, was not washed out at this duration. These results suggest that the critical threshold for autonomous biological recovery lies between three and ten days of high-intensity industrial loading, and that shock events exceeding five days at 1000 m³/day require active emergency WAS management to prevent prolonged non-compliance. This threshold information provides plant operators with a practical, evidence-based trigger criterion for activating emergency response protocols.

The simulated biological response to the shock load is consistent with documented full-scale incidents at WWTPs receiving uncontrolled industrial discharges. Kelly et al. [26] demonstrated that chemical inhibition of nitrifying bacteria under high organic loading causes nitrification rates to decline by 95–99%, consistent with the near-complete nitrification cessation observed here (from 1.05 to <0.001 mgN/L/h). Ayoub and El-Morsy [8] reported that extended-aeration systems exposed to high-COD industrial surcharges—particularly from food-processing industries—exhibited sludge settleability deterioration and secondary-clarifier overload within 24–48 h, mirroring the clarifier failure dynamics observed in this simulation. In the developing-country context specifically, Al-Wardy et al. [18] documented analogous organic-overload patterns at Iraqi municipal WWTPs receiving intermittent industrial inputs, with recovery timelines of two to four weeks following cessation of the discharge—broadly consistent with the post-shock recovery trajectory modeled for the JIFZ-WWTP under standard WAS management.

In summary, the shock-load scenario reveals that the JIFZ-WWTP lacks sufficient biological and physical buffering capacity to absorb uncontrolled high-strength industrial discharges, with nitrification failure and clarifier washout representing the most critical and operationally disruptive consequences.

3.5. Clarifier Behavior and Sludge Dynamics During the Shock Load

The final sedimentation tanks experienced severe overloading during the shock period in the simulation, as shown in Figure 9, Figure 10, Figure 11 and Figure 12. Influent TSS to the FSTs increased from 1047 mg/L to 2582 mg/L (+146%), while effluent TSS rose from 51 mg/L to 1659 mg/L, a 3156% increase that is consistent with sludge washout rather than progressive settling deterioration. State-point analysis confirmed that the applied solids loading rate exceeded the clarifier’s gravitational flux capacity, causing the sludge blanket to rise and breach the effluent weir in the model.

Contributing mechanisms included: (i) floc destabilization driven by the rapid growth of young, dispersed biomass under high-F/M conditions; (ii) potential filamentous proliferation in localized low-DO zones within the aeration tank; (iii) denitrification-induced rising sludge, as nitrate was consumed within the sludge blanket and the resulting N₂ gas became entrapped in floc particles, reducing their effective settling velocity; and (iv) pH-driven changes in floc surface charge associated with CO₂ outgassing and mineral precipitation. The concomitant collapse of denitrification—nitrate dropped from 37.3 to 0.004 mg/L during the shock—temporarily accelerated N₂ bubble formation within the clarifier, worsening the rising sludge phenomenon.

These mechanisms are inferred from the simulated bulk outputs together with established activated-sludge theory; they are not directly resolved by the model and should be read as plausible contributing processes rather than independently confirmed observations.

3.6. Operational Adjustments During and After the Shock Load

Two operational interventions were tested by simulation: (i) doubling the aeration airflow from 500 to 1000 m³/h per aeration tank, combined with an increase in the RAS rate; (ii) increasing the WAS rate from 60 to 300 m³/day as shown in Figure 13.

The aeration and RAS adjustment alone produced no measurable improvement in nitrification or clarifier performance, consistent with field experience and published case studies [26,27]. Since the primary limitation on nitrification was not oxygen availability per se but rather the biological suppression of autotrophic bacteria by the rapid heterotrophic growth, increasing DO supply could not restore the depleted nitrifier biomass on a short operational timescale. Additionally, elevating the RAS rate under conditions of poor sludge settleability increased the hydraulic load on the already overloaded clarifiers, exacerbating effluent TSS levels rather than improving them.

These interventions were quantified from the day-3 effluent under the sustained shock. Doubling the aeration airflow from 500 to 1000 m³/h left effluent quality essentially unchanged (total COD 8128 → 8120 mg/L; TSS 627 → 644 mg/L; NH₄-N 84.8 → 83.6 mg/L; DO already 10.3 mg/L), and increasing the RAS rate produced the same negligible effect (COD 8120 mg/L; TSS 642 mg/L; NH₄-N 83.8 mg/L). Dissolved oxygen remained non-limiting throughout the event (10.14 mg/L in the secondary clarifier both before and during the shock), so additional aeration could not improve performance. The nitrification failure was therefore not an oxygen-supply failure. Ammonia remained low (1.355 mgN/L) while nitrite accumulated to 81.9 mgN/L and nitrate fell from 37.32 to 0.004 mgN/L, indicating that ammonia oxidation continued while nitrite oxidation was selectively inhibited. This coincided with a rise in pH from 7.74 to 8.46 and in alkalinity from 30.7 to 524 mgCaCO3/L, conditions that raise free-ammonia concentrations and are known to inhibit nitrite-oxidizing bacteria preferentially. This interpretation is consistent with the model output but has not been validated experimentally and is offered as a hypothesis. By contrast, raising the WAS rate to 300 m³/day sharply reduced effluent solids (TSS falling from ≈630 to ≈80 mg/L within about a day), while effluent COD remained high (~7600 mg/L) only until the industrial discharge subsided (

Table 10. In-shock intervention comparison, day-3 effluent concentrations.

Effluent Variable	Base Shock (Air 500)	Aeration Doubled (Air 1000)	RAS Increased (500)	WAS Raised to 300
Total COD (mg/L)	8128	8120	8120	~7600
Total cBOD5 (mg/L)	3458	3449	3449	declining
TSS (mg/L)	627	644	642	~80
NH4-N (mg/L)	84.8	83.6	83.8	declining
DO (mg/L)	10.26	10.36	10.36	≥10

).

Increasing the WAS rate to 300 m³/day, by contrast, produced a marked improvement in simulated treatment performance. Effluent COD, BOD5, and ammonia concentrations declined substantially within several days of the intervention, and the system recovered toward pre-shock effluent quality more rapidly. This operational outcome is consistent with the activated-sludge shock recovery literature: targeted sludge wasting removes the accumulation of inert, inhibited, and damaged biomass from the system, resets the mean sludge age downward, reduces the concentration of adsorbed inhibitory compounds, and creates conditions favorable for the rapid re-growth of active, healthy floc-forming organisms and nitrifiers [28,29]. The value of elevated WAS as a recovery strategy following industrial shock loading is corroborated by controlled laboratory experiments [30] and plant-scale case studies [27]. However, sustaining WAS at 300 m³/day, five times the design rate, carries practical consequences that this study did not size quantitatively: increased sludge-handling and dewatering demand, higher disposal costs, a reduced effective SRT that could, if prolonged, itself compromise nitrifier retention once populations recover, and possible operational strain on the sludge-handling infrastructure. These trade-offs should be considered when translating the WAS-elevation recommendation into an operating protocol.

In summary, elevated WAS management is the most effective short-term operational response to an industrial shock event simulated in this study, providing a practical intervention that can substantially shorten biological recovery timelines, provided its sludge-handling and cost implications are planned for in advance.

A single stream-removal comparison was run, in which the energy-drink processing stream was withdrawn from the shock load while elevated WAS management was retained; this produced the fastest recovery of effluent quality among the cases tested. Because the other four streams were not withdrawn individually, this comparison is consistent with, but does not establish, the dominance of the energy-drink stream. COD and BOD₅ returned to pre-shock levels, nitrification resumed as the oxygen balance improved, and clarifier performance stabilized. These results highlight the importance of sector-specific pretreatment requirements in the industrial zone operating agreements, with stringency warranted for food and beverage manufacturing operations.

The overall biological system displayed partial resilience to the shock event in the simulation, in the sense that no permanent biomass collapse was observed, as shown in Figure 14, Figure 15 and Figure 16: heterotrophic bacteria continued to grow throughout the shock period, the ammonia-oxidizer net growth rate remained marginally positive (1.66 to 4.27 mg COD/L.day); this is a growth rate and should not be read as an increase in standing nitrifier biomass relative to the incoming ammonia load, and denitrification rates, while incomplete, remained above zero. The absence of toxic-shock signatures in the model, such as an immediate drop in oxygen uptake rate, sudden nitrification collapse at low simulated toxin levels, or evidence of cell lysis in the heterotrophic population, suggests that the simulated shock was predominantly an organic-overload event rather than a toxic insult. This distinction is operationally significant, but it is a modeling artifact rather than an empirical finding: the simulated industrial stream includes sectors such as textile and tannery operations that, in reality, may contain inhibitory or toxic compounds (e.g., chromium, sulfides, dyes) that were not explicitly represented in this version of the model. This is stated here as an explicit limitation: the organic-overload conclusion applies to the model as configured, and should not be read as evidence that toxic shock is not a real risk for JIFZ-WWTP.

3.7. Feasibility, Cost Considerations, and Governance Framework for Proposed Mitigation Measures

The mitigation measures recommended in this study—mandatory industrial pretreatment, real-time influent monitoring, and anoxic reactor retrofitting—carry distinct cost profiles and implementation complexities that must be considered within the Palestinian institutional and economic context.

Industrial pretreatment at source (pH equalization, screening, dissolved air flotation, and COD reduction to domestic-equivalent levels) represents the highest capital cost per factory, typically in the range of USD 150,000–500,000 per installation depending on effluent volume and composition [22]. However, this cost is borne by individual industrial operators rather than the WWTP authority and can be partially offset through negotiated industrial zone operating agreements and international development financing, particularly relevant given the JIFZ’s German governmental development support through PIEFZA. Operationally, factory-level pretreatment units require trained personnel and regular maintenance, which represents a practical challenge in settings with limited technical workforce capacity; this operational and staffing challenge, together with the risk of inconsistent enforcement across many small operators, is among the most likely obstacles to implementing the proposed mitigation measures in practice. Phased implementation, prioritizing the highest-risk sectors (energy drink manufacturing, meat and dairy processing, and tanneries, as tentatively identified in Section 3.6), would reduce the aggregate upfront investment while targeting the most disruptive effluent streams first.

Real-time influent monitoring (COD, TSS, flow, and pH sensors at the WWTP headworks) is considerably less costly, with installed system costs typically in the range of USD 20,000–60,000 and represents the highest benefit-to-cost intervention available to the WWTP operator [11]. An automated early-warning system linked to these sensors would allow operators to initiate emergency WAS elevation or influent diversion before biological damage accumulates—the operational equivalent of the simulation-based threshold findings.

The anoxic reactor retrofit required to address the TN removal deficit (Section 3.1) involves moderate capital investment, estimated at USD 80,000–200,000 for a reactor of appropriate volume given the JIFZ-WWTP’s flow range, plus ongoing costs for external carbon supplementation (methanol or acetate) of approximately USD 0.05–0.15 per cubic meter of treated wastewater [25]. While this adds operational cost, it is a prerequisite for achieving Class C effluent reuse compliance for TN, and therefore for sustaining the agricultural irrigation reuse pathway that is central to the plant’s long-term purpose.

From a governance perspective, the effectiveness of all proposed technical measures is contingent on a functioning regulatory and enforcement framework. The Palestinian Authority’s current institutional capacity for industrial discharge permitting and pretreatment compliance enforcement is limited, a challenge that is not unique to Palestine but is particularly acute given the political and financial constraints on the Palestinian Water Authority [11,15]. The annual deduction of approximately 100 million ILS from Palestinian tax revenues as a transboundary wastewater levy [15] provides a strong fiscal argument for investing in WWTP protection and pretreatment compliance: effective source control would reduce the effluent quality violations that contribute to transboundary wastewater costs and, by extension, to the financial penalty imposed on the Palestinian Authority. This policy linkage—between pretreatment enforcement, WWTP compliance, and the transboundary fiscal burden—constitutes a compelling governance rationale for prioritizing the recommended measures and for engaging international partners in supporting their implementation.

Pending full pretreatment and monitoring implementation, the immediate, lowest-cost precaution available to local authorities is to require factories in the highest-risk sectors identified above to hold effluent on-site (batch storage) rather than discharge continuously whenever an upset condition is suspected, and to notify the WWTP operator in advance of any planned high-strength discharge; for the surrounding population and downstream agricultural users, the corresponding precaution is to avoid drawing on JIFZ-WWTP effluent for irrigation during and immediately after any known or suspected shock event, until COD/TSS/TN return to verified compliant levels, given the compliance gaps this study identifies even under baseline conditions.

4. Conclusions

This study presents a dynamic simulation assessment of the Jenin Industrial Free Zone Wastewater Treatment Plant (JIFZ-WWTP), providing model-based indications of its operational performance envelope and shock-load vulnerability. Because the underlying model was calibrated on Jericho WWTP data and adapted, prior to commissioning, to JIFZ-specific conditions (Section 2.3), these findings should be read as simulation results requiring field confirmation once the plant is operational, not as validated operational results. They are organized below into main findings, practical recommendations, and explicit limitations.

5. Main Findings

(1) Under optimized steady-state simulation (RAS: 460 m³/day; WAS: 60 m³/day), the model indicates high removal efficiencies for TSS (95.18%), COD (89.5%), and cBOD5 (94.32%), and a maximum treatable influent flow of 1000 m³/day. Simulated effluent COD (109.2 mg/L) marginally exceeds the 100 mg/L Class C reuse target even as cBOD5 and TSS comply, so full Class C reuse compliance is not yet demonstrated without an additional polishing step. Total-nitrogen removal of 33.8% represents a further performance gap and should be addressed through the addition of an anoxic stage with external carbon dosing.

(2) Monte Carlo probabilistic analysis indicates that only 33% of simulated industrial influent scenarios comply with COD and cBOD5 standards simultaneously, and only 68% meet TN targets, underscoring the operational sensitivity of the system to industrial discharge variability and the value of probabilistic over single-scenario deterministic modeling for risk characterization, subject to the distributional-assumption sensitivity check recommended in Section 2.4.

(3) A simulated uncontrolled industrial shock load of 1000 m³/day over ten days causes near-complete nitrification failure, severe secondary-clarifier overload with sludge washout, and effluent concentrations of COD (~8000 mg/L), BOD5 (~3500 mg/L), and TSS (~510 mg/L) far exceeding Palestinian discharge limits. Recovery is gradual in the model, with biological phosphorus removal and nitrification the last functions to stabilize.

(4) A targeted elevation of the WAS rate to 300 m³/day following a shock event is the most effective short-term operational response simulated, outperforming increased aeration and RAS adjustment, though this response carries sludge-handling and cost consequences not sized in this study (Section 3.6).

5.1. Practical Recommendations

(a) Mandatory and rigorously enforced industrial pretreatment at source, covering as a minimum pH equalization, screening, dissolved air flotation (DAF), and COD reduction to domestic-equivalent levels before discharge to the common sewer, phased by sector risk.

(b) Installation of continuous influent monitoring at the WWTP headworks (COD, TSS, flow, and pH sensors) with automated early-warning protocols to allow operators to activate protective responses, bypass, holding, or emergency WAS adjustment, before biological damage accumulates.

(c) Introduction of an anoxic reaction zone with external carbon supplementation to improve the robustness of nitrogen removal against influent variability. Because the simulated baseline TN of 42.39 mg/L already meets the 45 mg/L Class C limit, this is a reliability measure rather than a prerequisite for compliance; together with a polishing step it would also address the marginal COD exceedance identified in this study.

(d) Development of a documented shock-load emergency protocol specifying pre-set thresholds for increased WAS rate, aeration adjustment, and influent diversion, with regular operator training to ensure effective and timely implementation.

5.2. Limitations

The following limitations apply to all findings above and should be considered before they are used for design or regulatory decisions: (i) the model was calibrated on a surrogate plant (Jericho WWTP) rather than on operational JIFZ-WWTP data, and has not yet been field-validated at JIFZ; (ii) the influent composition used for JIFZ scenarios is based on PIEFZA design documentation and engineering judgment rather than measured industrial discharge; (iii) the Monte Carlo analysis uses uniform input distributions and varies only four parameters, holding others fixed, both simplifications requiring the sensitivity checks noted in Section 2.4; (iv) toxicity from industrial compounds (e.g., heavy metals, chromium, biocides) was not explicitly modeled, so the organic-overload conclusion in Section 3.6 applies to the model as configured and should not be read as ruling out toxic-shock risk in practice; and (v) cost and governance recommendations in Section 3.7 are conceptual, order-of-magnitude estimates rather than site-specific costings.

Future work should focus on the integration of real-time sensor data with the GPS-X model to build a predictive digital twin once JIFZ-WWTP is operational; field validation of the calibrated parameter set against measured JIFZ data; explicit modeling of industrial-compound toxicity; disaggregated, stream-by-stream shock-load modeling to confirm the preliminary energy-drink-effluent finding (Section 3.6); evaluating bioaugmentation as a nitrification recovery strategy; and developing governance mechanisms, including legally binding pretreatment standards and discharge permit conditions, to institutionalize the technical safeguards identified in this study.